Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and...

Complexity of SeismicTime Series

Measurement and Application

Edited by

Tamaz ChelidzeM Nodia Institute of Geophysics Tbilisi Georgia

Filippos VallianatosTechnological Educational Institute of Crete Laboratory of

Geophysics and Seismology UNESCO Chair on Solid Earth Physics andGeohazards Risk Reduction Crete Greece

Luciano TelescaNational Research Council Tito Italy

11Are Seismogenetic Systems Randomor Organized A Treatise of TheirStatistical Nature Based on theSeismicity of the North-NortheastPacific Rim

Andreas Tzanis1 Angeliki Efstathiou1 Filippos Vallianatos21NATIONAL AND KAPODISTRIAN UNIVERSITY OF ATHENS ATHENS GREECE

2TECHNOLOGICAL EDUCATIONAL INSTITUTE OF CRETE AND UNESCO CHAIR ON SOLIDEARTH PHYSICS AND GEOHAZARDS RISK REDUCTION CHANIA CRETE GREECE

CHAPTER OUTLINE

111 Introduction 366

112 Nonextensive Approach to the Statistical Physics of Earthquakes 370

1121 Brief Exposeacute of NESP 370

1122 Seismicity and NESP An Overview 373

1123 Multivariate Earthquake Frequency Distributions Construction andNESP-Based Modelling 375

113 Earthquake Data and Analysis 379

1131 Earthquake Source Areas and Catalogues 379

1132 California 379

1133 Declustering 388

114 Results 390

1141 Determination of Randomness Thresholds 391

1142 Entropic Indices 392

115 Discussion 405

Acknowledgements 412

References 412

111 IntroductionSeismogenetic systems are generally thought to comprise a mixture of processes that expressthe continuum of tectonic deformation (background process) and a large population of after-shocks that express the short-term activity associated with the occurrence of significantearthquakes (foreground process) Although progress has been made in understanding theforeground process the statistical physics of background seismicity the nature of seismoge-netic system remains ambiguous

There are two general theoretical frameworks to describe the statistics of (background)seismicity The first (and currently most influential) postulates that the expression of thebackground process is Poissonian in time and space and obeys additive BoltzmannGibbsthermodynamics In consequence it expects background earthquakes to be statistically inde-pendent and while it is possible for one event to trigger another it submits that this occursin an unstructured way and does not to contribute to the long-term evolution of seismicityThus according to the lsquoPoissonian viewpointrsquo seismogenesis should be a memoryless pro-cess The most influential realization of the Poissonian paradigm is the ETAS model(Epidemic-Type Aftershock Sequence eg Ogata 1988 1998 Zhuang et al 2002Helmstetter and Sornette 2003 Touati et al 2009 Segou et al 2013) which essentially is aself-excited conditional Poisson process (Hawkes 1972 Hawkes and Adamopoulos 1973Hawkes and Oakes 1974) ETAS posits that randomly occurring background earthquakestrigger aftershocks and aftershocks trigger their own aftershocks thus spawning a short-termproliferation of clustered foreground events (aftershock sequences) whose number decaysaccording to the OmoriUtsu power-law (eg Utsu et al 1995) Proxy-ETAS models(Console and Murru 2001) as well as point process models to address the problem of inter-mediate to long-term clustering have also been developed such as EEPAS (Each Earthquakeis a Precursor According to Scale eg Rhoades and Evison 2006 Rhoades 2007) and PPE(Proximity to Past Earthquakes eg Marzocchi and Lombardi 2008)

At this point it is important to point out that Poissonian models are mainly concernedwith the statistics of time and distance between events The size (magnitude) distribution ofboth background and foreground processes is still thought to be governed by the time-honoured frequencymagnitude (FM) relationship of Gutenberg and Richter Howeverthe scale-free grading between earthquake frequency and magnitude implied by the FMrelationship is a power-law that cannot be derived from the BoltzmannGibbs formalismLikewise the OmoriUtsu formula is a ZipfMandelbrot power-law and is therefore incon-sistent with the BoltzmannGibbs formalism The heavy reliance of Poissonian seismicityworldviews and models on irrefutable yet evidently non-Poissonian empirical laws is anapparent contradiction (self-inconsistency) with no theoretical resolution it shows thatPoissonian seismicity models are effectively ad hoc conceptual constructs that try to recon-cile the (inherited) Poissonian worldview of statistical seismology with the obviously non-Poissonian dynamics of fault formation and clustering

The second framework also comprises different classes of models and proposes that theseismogenetic system is complex A well-studied class of models generically known as self-

366 COMPLEXITY OF SEISMIC TIME SERIES

organized criticality (SOC) proposes that seismicity is the expression of a nonequilibratingfractal active fault network that continuously evolves toward a stationary critical state withno characteristic spatiotemporal scale in which events develop spontaneously and any smallinstability has a chance of cascading into global failure (eg Bak and Tang 1989 Sornetteand Sornette 1989 Olami et al 1992 Sornette and Sammis 1995 Rundle et al 2000 Baket al 2002 Bakar and Tirnakli 2009 etc) According to Hanken (1983) the macroscopicproperties of a self-organized system may change with time due to perturbations in its possi-ble microscopic configurations but the system as a whole will remain in or continuously tryto reach the critical state The advantage and allure of SOC is that it is consistent both withitself and with several observed properties of earthquake occurrence the GutenbergRichterlaw the OmoriUtsu law and other power-law distributions of parameters pertaining to thetemporal and spatial expression of a simulated fault network emerge naturally during theevolution of simulated fault networks A variant of SOC is self-organizing criticality leading tocritical point behaviour at the end of an earthquake cycle (eg Sornette and Sammis 1995Rundle et al 2000 Sammis and Sornette 2001 and many others) This has been influentialduring the late 1990s and early 2000s but is no longer pursued as it made specific predic-tions (acceleration of seismic release rates) that could not be verified experimentally In thecontext of criticality the dependence between successive earthquakes (faults) is known ascorrelation this involves a long-range interaction and endows the seismogenetic system withmemory that should be manifest in power-law statistical distributions of energy release tem-poral dynamics and spatial dependence

A few authors have investigated models with alternative complexity mechanisms that donot involve criticality yet maintain the fault system in a state of nonequilibrium a list can befound in Sornette (2004) and a comprehensive discussion in Sornette and Werner (2009) Ina more recent development Celikoglu et al (2010) applied the Coherent Noise Model(CNM) (Newman 1996) based on the notion of external stress acting coherently on all agentsof the system without having any direct interaction with them The CNM was shown to gen-erate power-law behaviour in the temporal expression of its agent interevent time distribu-tions but has a rather weak point it does not include some geometric configuration of theagents and it is not known how this would influence the behaviour of the system

The Poissonian and complexitycriticality viewpoints agree that the foreground processcomprises a set of dependent events but the former assigns only local significance to thisdependence while criticality considers them to be an integral part of the regional seismoge-netic process In practice the fundamental difference between the two approaches is in theirunderstanding of the background process The former assumes that there is no correlation(interaction) between random background events and argues that their statistical manifesta-tions should best be described with the exponential and Gaussian distributions (consistentwith the BoltzmannGibbs thermodynamic formalism) Criticality requires short- and long-range interactions (correlation) between near or distal backgroundbackground back-groundforeground and foregroundforeground events leading to power-law distributions ofits temporal and spatial dynamic parameters Moreover noncritical complexity models can-not develop power-law distributions unless they evolve in nonequilibrium states meaning

Chapter 11 bull Are Seismogenetic Systems Random or Organized 367

that even in this case correlation is unavoidable It is therefore clear that if it is possible toidentify and remove the foreground process (aftershocks) it would also be possible to clarifythe nature and dynamics of the background process by examining its spatiotemporal charac-teristics for the existence of correlation It should also be apparent that in order to success-fully pursue this line of inquiry one must have a natural self-consistent general theoreticalframework on which to base the search for the existence of correlation (and not model-based or ad hoc conceptual constructs) One also requires effective measures of correlationin the temporal and spatial expression of seismicity as well as effective methods to separatethe background and foreground processes As it turns out there are (nearly) satisfactoryanswers to all three requirements

Nonextensive statistical physics (NESP) is a fundamental generalized conceptual frame-work to describe nonadditive (nonequilibrating) systems in which the total (systemic)entropy is not equal to the sum of the entropies of their components The concept has beenintroduced by Constantino Tsallis (Tsallis 1988 2001 2009 Tsallis and Tirnakli 2010) as ageneralization of the BoltzmannGibbs formalism of thermodynamics As such it comprisesan appropriate tool for the analysis of complexity evolving in a fractal-like space-time andexhibiting scale invariance long-range interactions and long-term memory (eg Gell-Mannand Tsallis 2004) NESP predicts power-law cumulative probability distributions in nonaddi-tive (nonextensive)1 systems which reduce to the exponential cumulative distribution in thelimiting case of additive (extensiverandompoint) processes NESP has already been appliedto the statistical description of seismicity with noteworthy results (see Section 1122) It hasalso been shown to generate the GutenbergRichter frequencymagnitude distributionfrom first principles (Sotolongo-Costa and Posadas 2004 Silva et al 2006 Telesca 20112012) In conclusion NESP provides a general complete consistent and model-independentcontext in which to investigate the nature and dynamics of seismogenetic processes

A definite indicator of correlation (interaction) between faults is the lapse between conse-cutive earthquakes above a magnitude threshold and over a given area this is referred to asinterevent time waiting time calm time recurrence time etc Understanding the statistics ofearthquake frequency versus interevent time is apparently essential for understanding thedynamics of the active fault network For that reason the frequencyinterevent time (FT)distribution has been studied by several researchers Almost every study hitherto has focusedon foreground and mixed backgroundforeground processes Empirical FT distributionsgenerally exhibit power-law characteristics and fat tails For that reason in the context of sta-tistical seismology they have been analysed with tailed standard statistical models reducibleto power laws in some way or another Examples of this approach are the gamma distribu-tion and the Weibull distribution (eg Bak et al 2002 Corral 2004 Davidsen and Goltz2004 Martinez et al 2005 Talbi and Yamazaki 2010) Some researchers working from a sta-tistical physics vantage point proposed ad hoc mechanisms for the generation of power laws

1 The term ldquoextensiverdquo (fullcomplete according to Merriam-Websterrsquos definition) was used by Tsallis (1988) todesignate systems that are equilibrating as opposed to those that are not (incomplete ie nonextensive) The termsldquoadditiverdquo and ldquononadditiverdquo are probably more appropriate but for consistency we adopt Tsallisrsquos terminology

by a combination of correlated aftershock and uncorrelated background processes (egSaichev and Sornette 2013 Hainzl et al 2006 Touati et al 2009) Nevertheless Molchan(2005) has shown that for a stationary point process if there is a universal distribution ofinterevent times then it must be an exponential one Investigations performed in the contextof NESP are reviewed in Section 1122 A second measure of fault interaction is the hypocen-tral distance between consecutive earthquakes above a magnitude threshold and over agiven area (interevent distance) The statistics of the frequencyinterevent distance (FD)distribution should be related to the range of interaction over that area unfortunately it isnot fully understood as it has been studied by less than a handful of researchers (eg Enevaand Pavlis 1991 Abe and Suzuki 2003 Batak and Kantz 2014 Schoenball et al 2015) Athird criterion of correlation (albeit not commonly acknowledged as one) is the b value ofthe GutenbergRichter frequencymagnitude (FM) distribution which expresses the scal-ing of the size-space of active faults over a given area (fault hierarchy) and conveys informa-tion about their distribution in space and the homogeneity of the domain they occupy TheFM distribution is static and does not say much about the dynamics of the fault networknor about correlations in the energy released by successive earthquakes Nevertheless thisundisputable empirical relationship is a standard against which to compare and test anyphysical and statistical description of the scaling of earthquake sizes and as such will beused herein

The discrimination between background and foreground processes is commonly referredto as declustering and can be carried out with deterministic or stochastic methods the latterbeing generally more efficient An excellent review of the subject can be found in vanStiphout et al (2012) Herein we have chosen to implement the stochastic declusteringmethod of Zhuang et al (2002) full justification is given in Section 1133 and is based on asignificant (for our objective) property the method is paradigmatic realization of the self-excited Poisson process as it implements the ETAS model in order to optimize the probabil-ity of aftershock identification Accordingly if the background seismicity obeysBoltzmannGibbs statistics then the Zhuang et al (2002) method should be able to extracta nearly random background process against which to test alternative hypotheses If it doesnot the argument in favour of a complex background would be stronger

This chapter is an attempt to examine the dynamics of seismogenesis by studying thelocal and regional statistical characteristics of earthquake occurrence in different seismoge-netic areas and implementing the generalized NESP formalism for the search for signs ofrandomness or self-organization in the probability distributions of event size interevent timeand interevent distance In order to ensure the rigour of our analysis instead of consideringonly one-dimensional earthquake frequency distributions as almost all studies have donethus far we will focus on multivariate distributions that express the joint probability ofobserving an earthquake larger than a given magnitude after a given time lapse and beyonda given distance thereby introducing additional mutual constraints on the permissible varia-tion of the relevant parameters

Our analysis will focus on the principal seismogenetic zones of the north and northeastPacific Rim specifically the Californian and Continental Alaskan transformational plate

margins and the AlaskanAleutian convergent plate margin These areas were chosen notonly for their longstanding reliable earthquake monitoring services and seismological catalo-gues but mainly because they comprise three different seismotectonic contexts in whichthere is (1) lithospheric seismogenesis along transform faults (2) lithospheric seismogenesisalong a convergent margin and (3) large-scale deep focus seismogenesis in and around amajor subducting slab The seismogenetic systems of California are all crustal earthquakesoccur mostly in the schizosphere ie in the rigid brittle part of the upper lithosphere Onthe other hand the Alaskan and AlaskanAleutian systems are both crustal and subcrustalin consequence the analysis of the matter will proceed by crudely separating crustal andsubcrustal earthquakes according to the depth of the Mohorovicic discontinuity This type ofdifferential study will also provide the opportunity to begin an inquiry as to whether environmentalconditions (eg temperature pressure) orand boundary conditions (free at the surface versus fixedat depth) have a role in the dynamic expression and evolution of the seismogenetic fault networkThe comparison of results from such exercises may afford for the first time evidence as to theexistence of differences between crustal and subcrustal seismogenesis and in the case of an affir-mative answer as to the origin of the differences and the cause of complexitycriticality thereof

112 Nonextensive Approach to the Statistical Physicsof Earthquakes

1121 Brief Exposeacute of NESP

In statistical mechanics an N-component dynamic system may have W5NΠiNi micro-scopic states where i ranges over all possible conditions (states) In classical statisticalmechanics the entropy of that system S is related to the totality of these microscopic statesby the Gibbs formula S5 2 k

Pipiln(pi) where k is the Boltzmann constant and pi is the

probability of each microstate Furthermore if the components of the system are all statisti-cally independent and uncorrelated to each other (noninteracting) the entropy of the systemfactorizes into the product of N identical terms one for each component this is theBoltzmann entropy SB52Nk

Pipiln(pi) It is easy to see that one basic property of the

BoltzmannGibbs formalism is additivity (extensivity) the entropy of the system equals thesum of the entropy of their components In the past few decades it has been widely appre-ciated that a broad spectrum of nonequilibrating natural and physical systems does not con-form to this requirement Such nonadditive systems which are also commonly referred to asnonextensive after Tsallis (1988) include statistically dependent (interacting) components inconsequence of which they acquire memory and can no longer be described withBoltzmannGibbs (BG) statistical physics

An appropriate thermodynamic description of nonextensive systems has been pioneeredby Tsallis (1988 2009) who introduced the concept of NESP as a direct generalizationof BoltzmannGibbs statistical physics Letting x be some dynamic parameter the

nonequilibrium states of nonextensive systems can be described by the Tsallis (1988) entro-pic functional

SqethpTHORN5k

q2 112

ethN0

pqethxTHORNdx

eth111THORN

where p(x)dx is the probability of finding the value of x in [x x1 dx] so thatETHWp(x)dx5 1

and q is the entropic index In the limiting case q1 Eq (111) converges to theBoltzmannGibbs functional

SBG 52 kethWpethxTHORNln pethxTHORNeth THORNdX eth112THORN

Like the BoltzmannGibbs the Tsallis entropy is concave and fulfils the H-theorem butis not additive when q 6frac14 1 For a mixture of two statistically independent systems A and Bthe Tsallis entropy satisfies

Sq ABeth THORN5 Sq Aeth THORN1 Sq Beth THORN1 12 qeth THORNSq Aeth THORNSq Beth THORN

This property is known as pseudoadditivity and is further distinguished into superadditiv-ity (superextensivity) if q 1 additivity when q1 (ie BoltzmannGibbs statistics) andsubadditivity (subextensivity) if q 1 Accordingly the entropic index is a measure of nonex-tensivity in the system

An additional feature of NESP is the generalization of the expectation value in accordancewith the generalization of entropy Thus the q-expectation value of x is defined as

hxiq 5ethN0

x pqethxTHORNdx eth113THORN

where

pqethxTHORN5frac12pethxTHORNqETHN

0 frac12pethx0THORNqdx0 eth114THORN

is an escort distribution The concept of escort distributions was introduced by Beck andSchloegl (1993) as a means of exploring the structures of (original) distributions describingfractal and multifractal nonlinear systems the parameter q behaves as a microscope forexploring different regions of p(x) by amplifying the more singular regions for q 1 and theless singular for q 1

Maximization of the Tsallis entropy yields the probability density function

pethxTHORN5 1

Zqexpq 2

λIq

x2 hxiq

eth115THORN

Zq 5

ethN0

expq 2λIqU x2 hxiq

dx

Iq 5ethN0

frac12pethxTHORNqdx

where λ is an appropriate Lagrange multiplier associated with the constraint on the q-expec-tation value and expq() denotes the q-exponential function

expqethzTHORN5 11eth12qTHORNzeth THORN1

12q11 eth12 qTHORNz 0

0 11 eth12 qTHORNz 0

8lt eth116THORN

that comprises a generalization of the exponential function for q1 expq(z)ezEq (115) is a q-exponential distribution and as is evident from the definition of

Eq (116) it is a power-law if q 1 corresponding to subextensivity (subadditivity) an expo-nential if q5 1 corresponding to extensivity (additivity) and a power-law with cut-off if 0 q 1 corresponding to superextensivity (superadditivity) in the last case the cutoff appears at

xc 5x0

12 q x0 5 Iqλ21 1 eth12 qTHORNhxiq eth117THORN

Using the definitions of x0 from Eq (117) and the q-expectation value from Eq (114)the probability pethxTHORN can be expressed as

pethxTHORN5 expq x=x0

ETHN0 expq x0=x0

dx0

eth118THORN

In the NESP formalism the theoretical distribution to be fitted to the observed (empirical)distribution of x is not the original stationary distribution pethxTHORN but the escort probabilitypqethxTHORN Accordingly the cumulative probability function (CDF) becomes

Peth xTHORN5ethNx

pqethx0THORNdx0 eth119THORN

By substituting Eq (118) into Eq (114) and evaluating the integral Eq (119) reduces to

Peth xTHORN5 expq 2xx0

5 12eth12qTHORN x

x0

112q

eth1110THORN

which also is a q-exponential distribution that for q 1 defines a CDF of theZipfMandelbrot kind

Fig 111 illustrates the behaviour of a q-exponential CDF (Eq 1110) for different values ofq For q 1 the CDF has a tail that becomes increasingly longer (fatter) with increasing q this

translates to increasing correlation (interaction) between its components and long-term mem-ory For q1 the power law converges to the common exponential distribution so that the sys-tem comprises an uncorrelated and memoryless point (random) process For 0 q 1 theCDF is a power-law exhibiting a cutoff whenever the argument becomes negative iePeth xTHORN5 0 and is characterized by a bounded correlation radius

1122 Seismicity and NESP An Overview

During the past several years NESP has enjoyed increasing attention with several research-ers studying the properties of the FT and FM distributions (eg Vallianatos and Telesca2012) This includes studies of simulated q-exponential distributions emerging from criticalseismicity models (eg Caruso et al 2007 Bakar and Tirnakli 2009) noncritical models(eg Celikoglu et al 2010) and rock fracture experiments (eg Vallianatos et al 2012a) Italso includes empirical studies of interevent time statistics based on the q-exponential distri-bution specified by Eq 1110 (eg Abe and Suzuki 2005 Carbone et al 2005 Vallianatoset al 2012b 2013 Michas et al 2013 2015 Papadakis et al 2013 2015 Vallianatos andSammonds 2013 Antonopoulos et al 2014) A recent review of NESP applications over abroad spectrum of scales from tectonic plates to country rock fractures and laboratory frag-mentation experiments is given by Vallianatos et al (2016)

Nonextensive analysis of the FM distribution has been undertaken by Sotolongo-Costaand Posadas (2004) Silva et al (2006) and Telesca (2011 2012) These authors proposedNESP generalizations of the GutenbergRichter law based on physical models that considerthe interaction between two rough fault walls (asperities) and the fragments filling spacebetween them (fragment-asperity model) this interaction is supposed to modulate

FIGURE 11ndash1 Three realizations of the q-exponential CDF for q 1 (red line) q5 1 (blue line) and q 1 plotted inlinear (left) and double-logarithmic scale (right)

earthquake triggering In this model the generalized GutenbergRichter law is approachedby considering the size distribution of fragments and asperities and the scaling of size withenergy The transition from size to energy and magnitude distributions depends on howenergy scales with size and with magnitude

Sotolongo-Costa and Posadas (2004) assumed that the energy stored in the asperities andfragments scales with their linear characteristic dimension r (E ~ r) or equivalently with thesquare root of their areas σ (E ~ σ12) they also assumed that the magnitude scales withenergy as M ~ log(E) Darooneh and Mehri (2010) expand on the same model butassume that E ~ exp(σ1a) and M ~ ln(E) We propose that the above assumptions are notcompatible with the empirical laws of energymoment and momentmagnitude scaling inparticular (eg Lay and Wallace 1995 Scholz 2002) Silva et al (2006) revisited thefragment-asperity model and expressed Eq (1110) as

pethσTHORN5 1212q22q

σ2hσiq 1

12q

eth1111THORN

Assuming that the energy scales with the characteristic volume of the fragments (E ~ r3)so that E ~ σ32 because σ scales with r2 it is easy to see that (σ 2 hσiq)5 (Eα)23 with αbeing a proportionality constant between E and r This yields the energy density function

pethETHORN5 2

3UE21=3

α2=3

U 12

eth12qTHORNeth22qTHORN

E2=3

α2=3

2 112q

so that PethETHORN5NethETHORNN210 5

ETHNE pethETHORNdE where N ( E) is the number of events with

energy greater than E and N0 is the total number of earthquakes If the magnitude scaleswith energy as M~1=3logethETHORN for q 1

PethMTHORN5 NethMTHORNN0

5 1212qM22qM

U102M

α2=3

22qM12qM

13 eth1112THORN

Eq (1112) has been used to investigate the seismicity of different tectonic regions(Telesca 2010ab Telesca and Chen 2010 Esquivel and Angulo 2015 Scherrer et al 2015)Finally assuming E ~ r3 but that the magnitude scales with energy as M~2=3logethETHORN Telesca(2011 2012) has introduced a modified version of Eq (1112)

5 1212qM22qM

U10M

α2=3

22qM12qM

13 eth1113THORN

We suggest that this model by postulating that the energy released in the form of seismicwaves scales with the effective area of the fault (fragments and asperities) is consistent withthe empirical laws of energymoment and momentmagnitude scaling and is also compati-ble with the well-studied rate-and-state friction laws of rock failure In consequence ouranalysis will be based on the FM distribution specified by Eq (1113)

1123 Multivariate Earthquake Frequency Distributions Constructionand NESP-Based Modelling

Our goal is to investigate whether seismicity is a Poissonian or complexcritical process byusing the NESP formalism to search for the presence (or absence) of correlation in time sizeand space This can be done by determining the values and variation of the relevant entropicindices To ensure rigour in our analysis instead of considering only one-dimensional fre-quency distributions as almost all studies thus far have done we focus on multivariate earth-quake frequency distributions thereby introducing additional mutual constraints on thepermissible variation of the empirically determined entropic indices The most general multi-variate earthquake frequency distribution is one that expresses the joint probability ofobserving an earthquake larger than a given magnitude after a given lapse time and beyonda given distance This would require the construction and analysis of trivariate frequen-cymagnitudeinterevent timeinterevent distance (FMTD) distributions which livein a four-dimensional realm and would be more difficult to manage and interpretAccordingly we opted to use the easier to handle bivariate frequencymagnitudeintere-vent time (FMT) distributions in order to focus on correlations in earthquake size andtime of occurrence However because this may not extract explicit information about therange of possible correlations we shall also use the interevent distance as a spatial filter bywhich to separate and study the temporal correlation of proximal and distal earthquakesThe rationale behind this approach is that if distal earthquakes are correlated in time thenthey have to be correlated in space via long-distance interaction and vice versa

A bivariate FMT distribution can be constructed as follows A threshold (cutoff) magni-tude Mth is set and a bivariate frequency table (histogram) representing the empirical incre-mental distribution is first compiled The empirical cumulative distribution is then obtainedby backward bivariate summation according to the scheme

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

τ5 1 DT m5 1 DM eth1114THORN

where H is the incremental distribution DM is the dimension of H along the magnitude axisand DT is the dimension of H along the Δt axis In this construct the cumulative frequency(earthquake count) can be written thus N(M$Mth Δt M$Mth) Then the empiricalprobability P( M$Mth Δt M$Mth) is simply

N fM$Mth ΔtM $Mthgeth THORNN0

N0 5NethM 5Mth 0THORN5 NN eth1115THORN

An empirical cumulative FMT distribution constructed according to Eq (1114) is pre-sented in Fig 112 it is based on a subset of 3653 events extracted from the NCSN earth-quake catalogue published by the North California Earthquake Data Center using athreshold magnitude Mth534 over the period 19752012 and excluding the MendocinoFracture Zone (MFZ) (for details see Section 113) The distribution is shown in linear(Fig 112A) and logarithmic (Fig 112B) frequency scales and comprises a well-defined

surface in which the end-member (M $ Mth Δt5 0) is the one-dimensional empiricalGutenbergRichter law and the end-member (M5Mth Δt) is the one-dimensional frequen-cyinterevent time (FT) distribution

Assuming that magnitudes and interevent times are statistically independent namely thatthe hierarchy of the active fault network does not influence the sequence of events the jointprobability P(M Δt) factorizes into the probabilities ofM andΔt in the sense P(M Δt)5P(M) P(Δt) Then by implicitly identifying the empirical and escort probabilities we obtain

N fM $Mth ΔtM $Mthgeth THORNN0

5 1212qM22qM

U10M

α2=3

22qM12qM

13 U 12 eth12 qT THORNU

ΔtΔt0

1

12qT eth1116THORN

FIGURE 11ndash2 (A) Bivariate cumulative frequencymagnitudeinterevent time (FMT) distribution constructedaccording to Eq (1114) on the basis of 3653 events with ML $ 34 extracted from the NCSN earthquake cataloguesee text for details (B) As per (A) but in logarithmic frequency scale (C) As per (A) but including unpopulated binsin the summation ie using the scheme Nmτ 5

Pτj5DT

Pmi5DM

Hij instead of Eq (1114) (D) As per (C) but inlogarithmic frequency scale

where qM and qT are the entropic indices for the magnitude and interevent times respec-tively and Δt0 is the q-relaxation time analogous to the relaxation (characteristic) timeoften encountered in the analysis of physical systems On taking the logarithm and settinga5 log(N0) Eq (1116) becomes

logN fM $Mth ΔtM $Mthgeth THORN5

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

0 eth12 qT THORNΔt eth1117THORN

Eq (1117) is a generalized (bivariate) law of the GutenbergRichter kind in which

bq 522 qMeth THORNethqM 2 1THORN eth1118THORN

is the NESP generalization of the b value (also see Telesca 2012) Accordingly Eq (1117) isthe general model to be implemented in the ensuing analysis It may also be worth notingthat Eq (1117) has been applied to the analysis investigation of time dependence in thecharacteristics of complexitycriticality along the San Andreas Fault (SAF) (Efstathiou et al2015) as well as to a preliminary study of the spatiotemporal properties of seismicity inSouth California (Efstathiou et al 2016)

The logarithmic form of the distribution shown in Fig 112B can be approximated withEq (1117) using nonlinear least-squares Because the parameters are all positive and theentropic indices are bounded we implemented the trust-region reflective algorithm (egMoreacute and Sorensen 1983 Steihaug 1983) together with least absolute residual (LAR) mini-mization so as to suppress possible outliers The result is shown in Fig 113A The qualityof the approximation is excellent with a correlation coefficient (R2) of the order of 099 Themagnitude entropic index qM5151 so that bq 1 which compares well with b values com-puted with conventional one-dimensional techniques for the same data set The temporalentropic index qT is approximately 13 and indicates moderate subextensivity Fig 113Bpresents a succinct statistical appraisal of the result performed by fitting a normal location-scale distribution (dashed line) and a Studentrsquos t test location-scale distribution (solid line)to the cumulative probability of the sorted residuals (r) Approximately 85 of the residualpopulation for which Nr N 01 is normally distributed The short truncated tail forming atr 201 consists of 39 residuals (B16 of the population) and does not deviate signifi-cantly from normality The long tail forming at r 02 is fitted with neither the normal northe t-location-scale distribution however it consists of only seven residuals (287) andrepresents outliers effectively suppressed by the LAR procedure

It is interesting to note that outliers are mainly observed at the intermediate and largermagnitude scales and longer interevent times They frequently arise from minor flaws in thecatalogue (eg omitted (sequences of) events glitches in magnitude reporting etc) but insome cases they may comprise true exceptions to the continuum of the regional seismoge-netic process for instance they may correspond to rare externally triggered events Herein

we shall not be concerned with such details but it is interesting to point them out The exis-tence of outliers has compelled us to introduce a significant constraint in the construction ofthe FMT distribution according to Eq (1114) the cumulative distribution is formed bystacking only the populated (nonzero) bins of the incremental distribution Regardless of theorigin of the outliers their inclusion in the summation would have generated a stepwisefunction in which the unpopulated bins (unknown probability densities) lying between theoutliers and the populated bins would appear as patches of equal earthquake frequency(uniform probability) as illustrated in Fig 112CD In this case the high probability zonesof the empirical bivariate distribution would comply with well-specified laws but the lowerprobability zones would for some lsquounknownrsquo reason include uniform patches In

FIGURE 11ndash3 (A) The logarithmic scale FMT distribution of Fig 112B together with the model fitted usingEq (1117) the colourbar represents the number of events in logarithmic (frequency) scale (B) Probability analysisof the residuals (see Section 1122 for details)

one-dimensional distributions this effect may not influence parameter estimation by a signif-icant factor and is often neglected In multivariate distributions however in addition to theobvious absurdity it would be numerically detrimental

In a final note in order to distinguish between proximal and distal earthquakes andassess their correlation we apply the above modelling procedure to subsets of the cataloguein which earthquakes are grouped by interevent distance according to the rule

CfCDM MthΔdL ΔdΔdU g eth1119THORN

where C is the catalogue CD is the subset catalogue Δd is the interevent distance and ΔdLΔdU are the upper and lower group limits respectively This is equivalent to constructingand modelling the conditional bivariate cumulative distribution

Peth fM $MthΔtfrac12M $MthΔdL ΔdΔdU gTHORN eth1120THORN

as a proxy of the trivariate FMTD distribution

113 Earthquake Data and Analysis1131 Earthquake Source Areas and Catalogues

This study investigates the statistical nature of seismicity along the north and northeastPacific Rim focusing on the major earthquake source areas of California Alaska and theAlaskanAleutian Arc and Trench System as can be seen in the seismicity maps inFigs 114 and 115 A brief description of the tectonic settings of these areas is givenbelow as we consider it to be necessary in understanding the rationale by which we catego-rize and treat our data

1132 California

The most prominent and well-studied seismogenetic feature of California is the SAF Thiscomprises a NW to NNW oriented 1300 km long right-lateral transformational boundarybetween the Pacific plate to the west and the North American plate to the east and has gen-erated several large (M 7) earthquakes during the past two centuries (eg 1857 19061989 1992 and 1999) The SAF system (main and lsquosiblingrsquo faults) is generally thought to com-prise three major segments the Mojave segment in South California between Salton Sea(approximately 3336N 1157W at the SE corner of California) and Parkfield MontereyCounty (approximately 359N 1204W) the central segment between Parkfield andHollister (approximately 3685N 1214W) and finally the northern segment betweenHollister and through the San Francisco bay area up to the MFZ (offshore approximately4036N 1245W)

The MFZ is a WE right-lateral transformational plate boundary between the Pacificand Gorda plates off the coast of Cape Mendocino in northern California (eg Dickinson

FIGURE 11ndash4 (A) The seismicity of California as illustrated by mapping the epicentres of earthquakes included in the full NCSN catalogue (19682015M $ 3) and the full SCSN catalogue (19802015 M $ 26) The source areas treated herein are colour-coded as follows gGrey Mendocino Fracture Zone(MFZ) orange north segment of the San Andreas Fault (nSAF) light blue Central ValleySierra Nevada RangeWalker Lane (SNR) light green SouthCalifornia Seismic Region nSAF is the broader area of the San Andreas south segment ECSZ is the East California Shear Zone (B) as per (A) but forregional catalogues declustered at the φ $ 70 probability level

FIGURE 11ndash5 Seismicity recorded along the primary boundary of the Pacific and North American plates during 19682015 and used in the present analysis(AEC catalogue) The top row illustrates the epicentres of crustal (above Moho) earthquakes along the Queen CharlotteFairweather and Denali faultzones (light blue) and along the AlaskanAleutian Arc (orange) (A) shows the full catalogue and (B) the declustered catalogue (φ $ 70) The bottomrow illustrates the epicentres of subcrustal (below Moho) earthquakes along the Aleutian Subduction zone As before (C) shows the full catalogue and (D)the declustered (φ $ 70)

and Snyder 1979a Furlong and Schwartz 2004) It extends westward from itstransformtransformtrench junction with the SAF and the Cascadia subduction zone(Mendocino Triple Junction) to the southern end of the Gorda Ridge at approximately404N 1287W it then continues on as an inactive segment for several hundredkilometres The MFZ includes the most seismically active part of California (Yeats 2013)and according to Dengler et al (1995) the north coastal region accounted for about 25of the seismic energy released in California in a 50-year period

The SAF accommodates only about 75 of the total motion between the North Americanand Pacific plates The rest is accommodated by NNWSSE right-lateral deformation in anarea east of the Sierra Nevada mountain range called the Walker Lane or Eastern CaliforniaShear Zone (Wesnousky 2005 Guest et al 2007) The Walker Lane terminates between thePyramid Lake in Nevada and Lassen Peak in California approximately at 403N 1206Wwhere the Honey Lake Fault Zone meets the transverse tectonic zone forming the southernboundary of the Modoc Plateau and Columbia Plateau with the Great Basin Pease (1965)observed that the alignment of that transverse zone and the MFZ suggests that the formermight have once been the continental terminus of the MFZ

To further complicate things California is geologically divided into North and South bythe SWNE left-lateral Garlock fault which extends for approximately 250 km between itsjunction with the East California Shear Zone (ECSZ) at the north-eastern edge of the MojaveDesert (approximately 356N 1164W) and its junction with the SAF at Tejon Pass (approxi-mately 348N 1189W) This major tectonic boundary is believed to have developed inorder to accommodate the strain differential between the almost WE extension of theGreat Basin eastwards of the ECSZ (eg Wernicke et al 1988) and the NWSE right-lateraltransformation of the ECSZ and SAF Thus the right-lateral motion on the SAF and ECSZlocks up in the area of the Garlock where local variations in the mode of deformation andearthquake focal mechanisms are observed (eg Jones 1988 Hardebeck and Hauksson2001 Becker et al 2005 Fialko 2006) Between 377N and 351N the left-lateral motion ofthe Galrlock fault generates a restraining bend and a broad S-shaped westward displacementof the SAF known as the lsquoBig Bendrsquo

The above-outlined tectonic setting results in four distinct earthquake source areas asshown in Fig 114

1 The MFZ bounded by the coordinates 40N to 43N and 123W to 128W2 The central and northern SAF segments (henceforth nSAF) north of the Garlock Fault

between Parkfield and the MFZ For the purpose of this study the geographic borders ofnSAF are defined to the north by the line joining the northern terminus of the SAFShelterCove section (402N 1243W) the northern terminus of the Bartlett Springs Fault System(Lake Mountain fault) and the Battle Creek Fault (405N 1219W) to the east by the BattleCreek Fault the Foothills Fault system (roughly 393N 1188W) and the Kern Gorge faultand White Wolf fault zone (353N 1186W) and to the West by an imaginary line runningoffshore parallel to the Pacific Coast

3 The Central Valley and Sierra Nevada Range up to and including the Walker Lane(henceforth SNR) This extends northward of the Garlock Fault and behaves as a semirigid

microplate (Sierran microplate) whose interior (Central Valley) is characterized by theabsence of significant faults and large earthquakes (Goter et al 1994 Dixon et al 2000McCaffrey 2005 Saleeby et al 2009 Hammond et al 2012) In this study the geographicboundaries of SNR are defined to the north by the line joining the Battle Creek Fault and thenorthern termini of the Butt Creek and Almanor fault zones (roughly 445N 1212W) andthen up to 116W to the east by the 116W meridian to the south by the Garlock Fault andto the west by the White Wolf and Kern Gorge fault zones the Foothills Fault system and theBattle Creek Fault

4 In contrast to their distinct nature north of the Garlock Fault the SAF and ECSZ con-verge and are not as easy to distinguish south of the fault In consequence we will considerthat area (southern SAF segment and ECSZ) to comprise an integral seismogenetic entityand henceforth refer to it as the South California Seismic Region (SCSR) The north bound-ary of the SCSR begins at the western terminus of the Santa Ynez Fault ZonePacificSection which is a virtual extension of the Garlock fault (345N 1205W) it then runs southof Tejon Pass and parallel to the Garlock Fault up to approximately 355W 1163W past itseastern terminus It then turns south and runs eastward of the South Bristol Mts Fault(346N 1156W) to Yuma at the USMexico border (327N 1146W) It continues west-wards to approximately 32N 117W which is south of Tijuana Mexico and then to 32N119W off the west coast of Mexico Finally it turns north and runs parallel to the coastlineand west of the San Clemente and Santa Cruz islands to 345N

The earthquake data we utilized for the nSAF SNR and MFZ source areas were extractedfrom the regional earthquake catalogue of the North California Seismic Network (NCSN httpwwwNCSNorg)The data utilized for the SCSR source area were extracted from theregional catalogue of the South California Earthquake Data Centre (SCSN httpwwwdatascecorg) Details are given in Table 111 In both NCSN and SCSN catalogues mostearthquakes are reported in the ML and Mw magnitude scales while there is a considerablenumber of events in the duration (Md) and amplitude (Mx) scales The latter two have beenexhaustively calibrated against the ML scale Eaton (1992) has shown that they are within 5of the ML scale for magnitudes in the range 0555 and that they are virtually independentof the distance from the epicentre to at least 800 km In consequence Md and Mx are practi-cally equivalent to ML For the purpose of the present analysis Mw magnitudes were alsoconverted to ML using the empirical formula of Uhrhammer et al (1996) Mw 5ML(09976 0020) (00506 0131) Thus both the NCSN and SCSN catalogues were reducedto the ML scale and are homogeneous and complete for ML $ 30 and ML $ 26respectively

11321 Alaska and the AlaskanAleutian Arc and Trench SystemThe Aleutian Arc and Continental (mainland) Alaska source areas are bounded by the coor-dinates 50N to 70N and 196W to 126W The principal structural and geodynamic featureof this area which also defines the geographical borderline of the north Pacific Rim isthe boundary between the North American and Pacific plates (Fig 115) The eastern plateboundary is defined by the Queen CharlotteFairweather (QCF) dextral transform fault

system parallel to which the Pacific plate moves N-NW relative to the North American plateat a rate of approximately 50 mmyear The plate boundary transits from transformational toconvergent along a zone extending between 575N 137W and 59N 1455W in which theYakutat terrane accretes to the North American plate and complicates the interactionbetween the two plates the boundary then continues westwards as the Aleutian Arc andTrench system Landward of the QCF system and apparently related to the plate boundarylies the right-lateral Denali transform fault This is an arcuate feature running in a northwest-erly direction for approximately 750 km from about 59N 1353W to about 635N 147W itthen bends westwards and continues almost parallel to the plate boundary for an additional500 km to approximately 63N 1552W The Aleutian Arc and Trench extends for approxi-mately 3400 km from the northern end of the Queen CharlotteFairweather fault system inthe east (near 585N 137W) to a triple junction with the Ulakhan Fault and the northernend of the KurilKamchatka Trench in the west (near 56N 196W) Westward of the AlaskaPeninsula (Unimak Pass 557N 164W) it transits from continental in the east to intraocea-nic in the west Subduction along the Arc generates the Aleutian Volcanic Arc that extends asfar as 182W The motion of the Pacific plate is always to the N-NW but due to the arcuategeometry of the trench the relative velocity vector of the convergence changes from almosttrench-normal in the east (Gulf of Alaska) to almost trench-parallel in the west Along thecontinental part of the subduction the rate of convergence varies from 56 mmyear in theeast (Gulf of Alaska) to 63 mmyear in the west (near Unimak Pass) along the oceanic partof the subduction the rate varies from 63 mmyear in the east to 74 cmyear in the west(eg DeMets and Dixon 1999)

Table 11ndash1 Summary of the Earthquake Catalogues Used in the Present Analysis

Source AreaSource AreaCode

SourceCatalogue Period

Full Catalogues

DeclusteredCatalogues(φ $ 70)

Mcomp

NoEvents Mcomp

NoEvents

South California SeismicRegion

SCSR SCSN 19802015 26 20088 26 3339

San Andreas Fault NorthSegment

nSAF NCSN 19682015 30 8596 32 943

Sierra Nevada Range WalkerLane

SNR NCSN 19682015 30 4982 32 591

Mendocino Fracture Zone MFZ NCSN 19682015 30 3706 30 1755Continental Alaska Queen

Charlotte Fairweatherand Denali Fault Zones

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

Subcrustalearthquakes

ATD 1720 44 1381

For the most part seismicity in Alaska can be attributed to the plate boundary between thePacific and North American plates Most of the seismic energy is released by large events thatrupture large segments of the boundary and accommodate most of the motion between thetwo plates Within the North American plate (Continental Alaska) the highest seismicity ratesare observed in southern Alaska parallel to the plate boundary and decrease northwardsaway from it Fault-plane solutions of moderate earthquakes in south-central central andnorthern Alaska typically exhibit strikeslip kinematics with northwesterly to northerly com-pressional axes whereas solutions in west-central Alaska generally exhibit normal faulting withnortherly oriented tensional axes Thus with the exception of west-central Alaska both the dis-tribution of earthquake activity and the available focal mechanisms are qualitatively consistentwith the hypothesis that the seismicity of Continental Alaska originates in the interaction of thePacific Plate and North American plates (eg Page et al 1991 and references therein)Moreover it appears that the plate boundary is not composed of a single fault system butinvolves several secondary faults both seaward and landward of the primary boundary whichaccommodate a small fraction of the relative plate motion The Aleutian Arc and Trench sys-tem generates large numbers of earthquakes in the crust as well as in the subducting andoverriding plates Additionally many earthquakes are associated with the activity of theAleutian Volcanic Arc Most large earthquakes in the region have thrust mechanisms indicatingthat they occur on the plate interface However some shallow (30 km) events have eitherstrikeslip or normal faulting mechanisms Most of the normal faulting events occurring inthe Aleutian outer rise region are caused by the bending of the Pacific plate as it enters thetrench while most of the shallow strikeslip events are concentrated along the island axis

The earthquake data utilized for the source areas of Continental Alaska and the AleutianArc were extracted from the regional earthquake database of the Alaska Earthquake Center(httpwwwaeicalaskaeduhtml_docsdb2cataloghtml) and comprise a total of 48995events recorded in the area 50N to 70N and 196W to 126W over the period 19682015In the AEC catalogue the overwhelming majority of events are reported in the ML magnitudescale However a significant number are reported only in the surface (MS) and body wave(mb) magnitude scales On the bright side another significant number is reported in multiplemagnitude scales and of these 1715 are jointly reported in the ML MS and mb scales It istherefore straightforward to generate calibration tables by which to convert MS and mb toML This exercise was carried out by robust reweighted linear regression with a redescendingbisquare influence function The MLMS relationship is shown in Fig 116A and the result-ing regression (calibration) formula is

ML 5 eth10746 0018THORN3mb eth040996 00942THORN 4mb 72

The MLmb relationship is shown in Fig 116B and the corresponding regression for-mula is

ML 5 eth07126 0013THORN3MS 1 eth16516 0066THORN 35MS 75

The relationships between MLmb and MLMS are obviously linear so that the regressioncoefficients are rather precisely determined Thus acknowledging the problems associated

with the saturation of the local and body wave scales at the large magnitude end of the spec-trum and assuming that both relationships can be linearly extrapolated to smaller magni-tude scales it is possible to construct a homogeneous version of the AEC catalogue with allevents reported in the local magnitude scale

The AEC catalogue presents a conundrum Fig 116C clearly shows that FM distribu-tion of seismicity recorded along the Aleutian Arc as this is outlined in Fig 115 isbimodal a feature not present in the seismicity of Continental Alaska (Fig 116D) For mag-nitude scales between ML53 and 43 the b value is 047 and for ML $ 44 increases almostabruptly to 11 The origin of this bimodal distribution might be natural (different physical

FIGURE 11ndash6 (A) Relationship between local and surface wave magnitude scales and (B) between the local andbody wave magnitude scales for the area of Alaska and the Aleutian Arc Analysis based on 1715 events jointlyreported in the ML MS and mb magnitude scales in the catalogue of the Alaska Earthquake Center The regressionlines were fitted with robust linear least squares broken lines mark the 95 confidence limits (C) Thefrequencymagnitude distribution of seismicity along the Aleutian Arc and Trench (D) As per (C) for continentalAlaska Down-pointing solid triangles represent the incremental distribution solid squares represent thecumulative distribution broken lines are 99 confidence limits

mechanisms operating at small and intermediatelarge magnitude scales) although b valuesas low as 047 over so broad an area are not easy to explain On the other hand as can beseen in the incremental distribution (downward-pointing triangles in Fig 116C) the escala-tion of frequency is faltering between ML539 and 43 (events missing) and there is a rathersuspicious leap of about 5500 events between ML530 and 31 (event surplus) which is alsodifficult to explain naturally Given also is the relative sparsity and almost one-dimensionalgeometry of the monitoring network along the Aleutians (see httpsearthquakealaskaedunetwork) and the difficulties associated thereof with the detection of small earthquakesFinally it is not difficult to verify that bimodality is definitely more pronounced in the west-ern (oceanic) part of the convergence (west of Unimak Pass) where the network is mostsparse As a result we cannot be certain that the differences between the small and interme-diatelarge magnitude scales are natural and we cannot investigate this rather nontrivialissue in the space available here As a consequence and as far as the Aleutian Arc andTrench is concerned we shall only consider the intermediate and large earthquake popula-tion (ML $ 44) for which the FM distribution albeit imperfect does raise concerns aboutits constitution It is apparent that in that area the homogenized version of the AEC cata-logue is complete for ML $ 44 (Fig 116C) Conversely in Continental Alaska we shallconsider all earthquakes with magnitudes ML $ 3 for which the catalogue appears to becomplete (Fig 116D)

As is evident in the foregoing seismogenesis in Alaska and the Aleutian Arc develops in arather complex tectonic background extends over a very large area and range of depths andexhibits regional variation For these reasons it is not feasible to thoroughly examine theentire area of Continental Alaska and the Aleutian Arc Rather in keeping with the objectiveof studying the statistical nature of seismicity along the Pacific Rim we will limit our inquiryto the area of the principal tectonic feature of the Rim the broader boundary between theNorth American and Pacific plates In this area it is possible to distinguish three classes ofearthquake activity (1) crustal earthquakes in Continental Alaska primarily associated withthe eastern transformational plate margin (2) crustal earthquakes along theAlaskanAleutian Arc primarily associated with the convergent plate margin and (3) sub-crustal earthquakes along the AlaskanAleutian Arc associated with the subducting slabThis provides an opportunity to study and compare the statistics of earthquakes generated indifferent seismotectonic settings environmental (crust vs subducting slab) and boundaryconditions (free in the crust vs fixed in the slab) and to inquire whether these differencesaffect the dynamic expression of the fault network

Following the above reasoning we will inquire into the statistical nature of crustal seis-micity along the eastern transformational plate boundary defined by the QueenCharlotteFairweather and Denali faults in which we include the transitional zone spannedby the Yakutat Terrane as well as the Wrangelian Composite Terrane This area will hence-forth be referred to as the Queen CharlotteDenali zone or QCD We will also inquire intothe statistical nature of seismicity observed along the convergent plate boundary but in thiscase we will conduct a separate analysis of crustal and subcrustal earthquakes by crudely dis-tinguishing them according to the depth of the Mohorovicicdiscontinuity this is approximately

40 km beneath the Yakutat Terrane (Christeson et al 2013) and approximately 385 km along theAleutian Arc (Janiszewski et al 2013) The crustal seismicity and earthquake catalogues willhenceforth be referred to as ATC (Aleutian Trench Crustal) while their subcrustal counterpart willbe referred to as ATD (Aleutian Trench Deep) The epicentral distributions of the QCD ATC andATD earthquakes are illustrated in Fig 115 information about the respective catalogues is sum-marized in Table 111

1133 Declustering

The question of whether the background seismogenetic process is fundamentally random orcorrelated is open to debate and can be answered by analysing reduced versions of the earth-quake catalogues in which the aftershock sequences have been eliminated in as optimal away as possible The process of reducing an earthquake catalogue so as to separate back-ground and foreground events is referred to as declustering An excellent review of declusteringmethods and their evolution from deterministic (eg Gardner and Knopoff 1974 Reasenberg1985) to stochastic (eg Zhuang et al 2002 Marsan and Lenglineacute 2008) is given in vanStiphout et al (2012) The deterministic methods identify foreground events on the basis oftemporal and spatial windows that scale with the magnitude of the mainshock while ignoringaftershocks triggered by aftershocks (higher-order events) The stochastic methods allow formultiple generations of aftershock triggering within a cluster and use Omorirsquos law as a measureof the temporal dependence of aftershock activity Both approaches ignore fault elongationand assume circular (isotropic) spatial windows Stochastic declustering was introduced byZhuang et al (2002) their approach improves on previous methods because it optimizes thetemporal and spatial window in which to search for aftershocks by fitting an ETAS model tothe earthquake data Furthermore instead of assigning aftershocks to arbitrarily chosen main-shocks it assigns each earthquake in the catalogue with a probability that it is an aftershock ofits predecessor so that all earthquakes may be possible mainshocks to their short-term afteref-fects Marsan and Lenglineacute (2008) carried stochastic declustering one step further by introduc-ing a generalized triggering process that does not require some underlying earthquakeoccurrence model nevertheless they still assume that background earthquakes occur at a con-stant and spatially uniform rate Herein we have chosen to implement the method of Zhuanget al (2002) because it has an additional and significant advantage for our objectives it is aparadigmatic realization of the self-excited Poisson process Thus if the background seismicityobeys BoltzmannGibbs statistics this method should be able to extract a nearly randombackground process against which to test the alternative hypotheses If it is does not the argu-ment in favour of a non-Poissonian background would be stronger

The Zhuang et al (2002) method utilizes the following form of the normalized probability thatone event will occur in the next instant conditional on the history of the seismogenetic process

λ t x yM jHteth THORN5μ x yMeth THORN1Xiti t

κ Mieth THORN g t2 tieth THORN f x2 xi y2 yijMieth THORN jethMjMiTHORN

where λ is the conditional intensity on the history of observation Ht until time t μ(x y M) isthe background intensity κ(M) is the expected number of foreground events triggered by amagnitude M mainshock and g(t) f(xy|Mi) and j(M|Mi) are respectively the probability dis-tributions of the occurrence time the location and the magnitude events triggered by amainshock of magnitude Mi If the catalogue is arranged in chronological order then theprobability of an event j having been triggered by an event i j can be estimated from theoccurrence rate at its occurrence time and location as

pij 5κethMiTHORN gethtj 2 tiTHORN f ethxj 2 xi yj 2 yijMiTHORN

λethtj xj yjjHtTHORN

and the probability that event j is an aftershock is given by

pj 5Xj21

i51

pij

Conversely the probability that event j is background is given by

φj 5 12 pj 5μethxj yjjHtTHORNλethtj xj yjjHtTHORN

The algorithm runs iteratively through the catalogue and by assigning probabilities pij pjand φj to the jth event generates the foreground subprocess associated with the ith event(ie its aftershock sequence) It thus separates the catalogue into a number of subprocesseswhose initiating events comprise the background As a general rule events with φj 50are considered to be foreground

Since the output of stochastic declustering is not unique it is useful to use the probabili-ties pij and φj to generate different realizations of the declustered catalogue at different prob-ability levels and use them to test hypotheses associated with background seismicity andoraftershock clustering Our analysis herein will be based on the assumption that events withprobability φj $ 70 are likely to be background Results obtained from the NESP analysisof higher probability levels will not be shown here as they do not offer significant additionalinformation with respect to the objectives of this chapter

The results of our declustering exercise are summarized in Table 111 and illustratedin Fig 117 where the cumulative earthquake counts of the full earthquake cataloguesare shown with solid lines and the corresponding cumulative counts of their declusteredversions with broken lines It is apparent that all catalogues declustered at the φ $ 70level are almost free of the time-local rate jumps that indicate the presence of aftershocksequences therefore they are fairly representative of the background process It shouldbe noted however that they are not always completely smooth and exhibit small fluctua-tions because a small portion of the remaining events are residuals of the foregroundprocess

114 ResultsThe analysis and appraisal of our results will be based on the fact that NESP predicts correlation(dependence) between successive earthquakes that involve long-range interaction endows theseismogenetic system with memory and generates power-law statistical distributions of itsdynamic parameters The degree of correlation is measured by the entropic indices so that ifq 6frac14 1 the system is nonextensive whereas if q1 the system is Poissonian (uncorrelatedand memoryless) Because the appraisal of low-valued experimental realizations of q can beambiguous for obvious reasons we also require a measure (threshold) on the basis of which toconfidently infer whether a seismogenetic system is nonextensive or Poissonian Our answer tothis problem is reported in Section 1141

FIGURE 11ndash7 Cumulative event counts for the full (solid lines) and declustered (broken lines) earthquakecatalogues we have used herein (A) Full and declustered subcatalogues of North California (NoCal) see text fordetails (B) As per (A) for the South California Seismic Region (SCSR) (C) As per (A) for the crustal (ATC) andsubcrustal (ATD) catalogue subsets of the Aleutian Arc and Trench (D) As per (A) for the QueenCharlotteFairweather and Denali zone of transform faults (Alaska)

1141 Determination of Randomness Thresholds

In order to determine a threshold value above which it is safe to conclude that the tempo-ral entropic index (qT) indicates nonextensive seismogenetic processes we applyEq (1117) to the analysis of several background catalogues generated on the basis of theETAS model each of those catalogues should yield temporal entropic indices with anexpectation value of unity The synthetic catalogues were generated with the stochasticETAS aftershock simulator program lsquoAFTsimulatorrsquo of Felzer (2007) The program uses theGutenbergRichter and OmoriUtsu laws to simulate the statistical behaviour back-ground and foreground seismicity and Monte Carlo methods to simulate backgroundearthquakes as well as multiple generations of aftershocks Known mainshocks can beincluded as point or planar sources and background earthquakes are chosen randomlyfrom observed or contrived spatial distributions (grids) of earthquake rates This facilitatesthe generation of realistic synthetic background catalogues consistent with the knownlong-term seismotectonic characteristics of a given area (for details see Felzer et al 2002Felzer and Brodsky 2006) In our implementation of the AFTsimulator we have used theETAS parameterizations for North and South California obtained (fitted) by declusteringthe NCSN and SCSN catalogues We have also assumed a uniform backgroundseismicity rate such that b5 1 and have set the maximum expected magnitude to beML572 approximately the same as the maximum magnitudes observed in Californiaduring the 47-year period 19682015 (the Loma Prieta and Landers earthquakes of 1989and 1992 respectively)

Fig 118 illustrates results from NESP analysis of 40 synthetic background catalogues 20of which were compiled for the SCSR source area and 20 for the whole of North California(NoCal nSAF 1 SNR 1 MFZ) Both sets of catalogues span a period of 47 consecutiveyears Fig 118A illustrates the variation of the mean values hqTi and hqMi computed fromthe analysis of the synthetic catalogues together with their associated 3σ error margins as afunction of the threshold (cutoff) magnitude Mth It is apparent that all hqT(Mth)i are consis-tently lower than 11 without exception so that max[hqT(Mth)i1 3σ] 115 Likewise allhqM(Mth)i exhibit an almost imperceptible variation around 15 so that bq 1 consistentlywith the assumption on which the synthetic ETAS catalogues were constructed It is alsoapparent that the populations qT(Mth) and qM(Mth) from which hqT(Mth)i and hqM(Mth)ihave been derived are remarkably consistent the 3σ error bars are generally very small andin many cases smaller than the size of the symbols representing the expectation valuesFig 118B illustrates the variation of entropic indices computed by grouping the earth-quakes of the synthetic catalogues according to interevent distance (Eq 1119) and modellingthe conditional probability function expressed by Eq (1120) All results have been derivedby considering earthquakes above a threshold magnitude Mth530 As above thefigure shows mean values hqT(Δd)i and hqM(Δd)i with their associated 3σ error margins AllhqT(Δd)i are consistently low for all interevent distance groups so that max[hqT(Δd)i13σ] 12 while hqM(Δd)i are also very stable and exhibit small fluctuations around 15 sothat bq1 as expected

The above exercise was conducted with several random background catalogues generatedon the basis of the ETAS model In consequence it can be concluded that the analytical pro-cedure described in Sections 1122 and 1123 yields stable magnitude entropic indices andproxy b-values (bq) absolutely consistent with the assumptions on which the synthetic ETAScatalogues were constructed More importantly however the results establish that the sys-tematic observation of experimental values qT(Mth) $ 115 and qT(Δd)i 12 would becompelling evidence of nonextensive seismogenetic dynamics

1142 Entropic Indices

In order to conduct as comprehensive an analysis as possible we analyse full and declus-tered catalogues of the source areas specified in Section 1131 Basic information about the

FIGURE 11ndash8 NESP analysis of 20 ETAS synthetic background catalogues constructed with the characteristics ofSouth Californian (SCSR) and 20 constructed with the characteristics of North Californian seismicity (NoCal) Bothcatalogues span a period of 47 consecutive years (A) Mean values hqT(Mth)i and hqM(Mth)i of the entropic indicesand associated 3σ error margins computed for different threshold magnitudes (Mth) The horizontal dashed line atqT5115 marks the threshold above which qT(Mth) can be safely assumed to indicate non-Poissonian processes (B)Mean values hqT(Δd)i and hqM(Δd)i with associated 3σ error margins computed for different interevent distancegroups Δd The horizontal dashed line at qT512 marks the threshold above which qT(Δd) can be safely assumed toindicate non-Poissonian processes

relevant earthquake catalogues is provided in Table 111 The analysis focuses on the varia-tion of the entropic indices with respect to threshold magnitude (Mth) and interevent dis-tance (Δd) The results are summarized in Tables 112 and 113 and displayed inFigs 1191115 In order to maintain experimental rigour estimation of the entropic indi-ces is not performed for catalogue subsets containing less than 300 events and results are notconsidered and displayed unless associated with a goodness of fit (R2) better than 097

11421 California Full CataloguesAs can be seen in Fig 119A all magnitude entropic indices are quite stably and consis-tently determined qM(Mth) functions computed from the nSAF SNR and SCSR cataloguesare very comparable and vary between 148 at Mth 5 30 (bq 5 108) and B15 (bq 5 1) atMth 5 35 steadily increasing thereafter to 158 at Mth 5 43 (bq 5 072) The entropic index qMlike the b-value to which it is related represents the scaling of the size distribution of earth-quakes Here it indicates a subextensive scalefree process possibly associated with a changein the size distribution and spatial clustering of intermediatelarge magnitude events thatappears to become increasingly tighter Notably analogous changes are conspicuous in

Table 11ndash2 Range of Variation of the Entropic Indices Obtained From theEarthquake Source Areas of California

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Δd 100 km Δd 100 km

Full SCSR 108177 168122 114139 148158 108075 147152 116093nSAF 118137 164148 122141 148158 108075 146152 117092SNR 132152 146 154168 152156 093080 146158 116078MFZ 106132 123140 123133 156160 078067 153157 089075

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

Table 11ndash3 Range of Variation of the Entropic Indices Obtained From theEarthquake Source Areas of Continental Alaska and Aleutian Arc and Trench

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

FULL QCD (Mth $ 3) 112144 131144 126151 159161 070063 156161 077062ATC (Mth $ 44) 110131 131134 100133 152147 092114 154151 084096ATD (Mth $ 44) 100115 117 110132 153146 088116 151156 096079

DECLUSTEREDφ $ 70

QCD (Mth $ 3) 11138 NA 134137 151155 096084 151153 094087ATC (Mth $ 44) 129152 NA 145165 147140 111150 152148 091107ATD (Mth $ 44) 100110 NA 104117 150147 098111 152150 09210

conventional frequencymagnitude plots where they appear to commence after about M5Finally for the MFZ area qM (Mth) is estimated at the markedly higher level of 156160with a mean of hqMi-MFZ5 157 6 001 so that bq(Mth) A (078 067) Such qM and bqvalues indicate rather high levels of clustering in the MFZ active fault network

The variation of qM with interevent distance Δd is shown in Fig 119B It is apparentthat all qM(Δd) functions are rather stable over all interevent distances As before qM(Δd)functions computed for the nSAF SNR and SCSR full catalogues are comparable and gener-ally vary between 146 and 152 so that bq(Δd) varies between 117 and 092 Changes in scal-ing such as those observed in Fig 119A are not evident because the threshold magnitudeused in these calculations is considerably lower than the threshold of the changes Finallyfor the MFZ catalogue qM(Δd) is again higher than in all previous areas as it varies between157 and 153 so that bq(Δd) A (075 089) This shows that the high level of clustering

FIGURE 11ndash9 Analysis of the magnitude entropic index qM for the full catalogues of the seismogenetic systems ofCalifornia (see Section 11311 and Fig 114A for details) Panel (A) illustrates the variation of qM as a function ofthreshold magnitude (Mth) Panel (B) illustrates the variation of qM with interevent distance Δd binning schemesvary so as to maximize statistical rigour Confidence limits of 95 are also drawn but are not always visible as theycan be smaller than the symbols

inferred for the MFZ fault network from the analysis of Fig 119A persists over distances ofat least 400 km

The variation of the temporal entropic index with threshold magnitude is shown inFig 1110A Let us begin with the results from the full SCSR catalogue It is apparent thatqT(Mth) is lower than 12 at small magnitude scales but for Mth 34 increases steadily andsteeply to higher than 16 at Mth $ 42 Taken over the entire SCSR area small earthquakesappear to be uncorrelated possibly because very small events may be concurrently spawnedby different parental earthquakes at different distant locations of an extended seismogeneticarea many of these events have no causal relationship and when mixed and chronologicallyordered in a catalogue they may randomize the statistics of interevent times If this interpre-tation is correct it is all the more significant to point out that the increase in correlation with

FIGURE 11ndash10 Analysis of the temporal entropic index qT for the full catalogues of the seismogenetic systems ofCalifornia (see Section 1131 and Fig 114A for details) Panel (A) illustrates the variation of qT as a function ofthreshold magnitude (Mth) Panel (B) illustrates the variation of qT with interevent distance Δd binning schemesvary so as to maximize the statistical rigour Error bars represent 95 confidence limits they are not always visibleas they are frequently smaller than the symbols

magnitude which involves faults distributed over the entire seismogenetic area is com-pelling evidence of operational long-range interaction The results obtained from the SierraNevadaWalker Lane (SNR) full catalogue are very similar although here the increase inqT(Mth) with magnitude is smoother and milder than in SCSR the estimates of the temporalentropic index begin at the certainly higher level of 132 for Mth533 and ends at the level of151 for Mth5 42 Thus the SNR system which also bears evidence of long-range interac-tion appears to exist in a state of correlation stronger than SCSR

Results from the analysis of the nSAF and MFZ full catalogues are clearly different InMFZ qT(Mth) fluctuates around 12 so that hqT(Mth)i5 12 6 0067 but increases to 132 atlarger threshold magnitudes (Mth $ 41) exhibiting weak albeit persistent overall correla-tion In nSAF qT(Mth) is stably determined around a mean value of 129 6 004 for Mth

38 but decreases rapidly to 12 6 002 for Mth 38 It may come as a lsquosurprisersquo that thebehaviour of the temporal entropic index of nSAF is quite unlike that of SCSR and SNR itseems to imply that in adjacent lsquosiblingrsquo tectonic settings there can be fault systems simulta-neously operating at very different levels of self-organization This has prompted furtherscrutiny of the nSAF data set whose results are presented below as it turned out therersquosmore to this than meets the eye

FIGURE 11ndash11 Analysis of the full nSAF catalogue for the periods 196888 (top row) and 19902015 (bottom row)Panels (A) and (C) illustrate the variation of the entropic indices with threshold magnitude (Mth) Panels (B) and (D)illustrate the variation of the entropic indices with interevent distance (Δd) In all cases error bars represent 95confidence intervals

The variation of the entropic indices with earthquakes grouped according to intereventdistance (Δd) is shown in Fig 1110B When the analysis is carried out in this mode it isexpected that qT(Δd) will exhibit higher values at interevent distances shorter than 100 kmdue to the dominant effect of near-field interactions in aftershock sequences Such behaviouris observed in SCSR and nSAF for which qT(Δd 50 km) was determined to be 172 and164 respectively At longer interevent distances the results indicate moderate to weak corre-lation In SCSR qT(Δd 100 km) varies from 114 to 139 with a mean value of 122 6 01and exhibits a clear tendency to increase for Δd $ 300 km In nSAF qT(Δd 100 km) variesfrom 122 to 141 with a mean value of 129 6 008 it also tends to increase after 300 km In

FIGURE 11ndash12 Analysis of the magnitude (qM) and temporal (qT) entropic indices for the declustered catalogues ofthe seismogenetic systems of California (see Section 11311 and Fig 114B for details) (A) Variation of qM withthreshold magnitude Mth (B) Variation of qT with threshold magnitude (Mth) (C) Variation of qM with intereventdistance Δd (D) Variation of qT with interevent distance Δd In (C) and (D) binning schemes vary so as to maximizestatistical rigour Error bars represent 95 confidence limits they are not always visible as they are frequentlysmaller than the symbols

SNR the correlation is merely significant (qT 5 146) at interevent distances shorter than100 km but increases to strong (qT 154) for all Δd longer than that It is quite apparentthat the SCSR and nSAF systems which effectively are segments and branches of the SAFare rather similar in their behaviour The SNR system behaves in an opposite sense whichindicates that earthquake activity there including aftershock sequences is basically con-trolled by long-range interaction Finally in MFZ qT(Δd) is only moderate (13135) atshort interevent distances increasing to 146 in the interval 50150 km only to decreaseagain to the level of 125 for Δd 150 km (moderate long-range correlation)

Let us now focus on nSAF in an attempt to explain the divergent (with respect to SCSRand SNR) behaviour observed in Fig 1110A Fig 1111AB illustrates the analysis of annSAF subset catalogue spanning the period 196831 December 1988 Fig 1111CD are thesame for 1 January 19902015 The year not taken into consideration (1989) is the one lead-ing to the M7 Loma Prieta earthquake of 17 October 1989 and including the bulk of its

FIGURE 11ndash13 Analysis of the magnitude entropic index qM versus threshold magnitude for full (grey) anddeclustered (red) earthquake catalogues along the PacificNorth American plate boundary in Alaska and theAlaskanAleutian Arc (see Section 11312 and Fig 115 for details) Panel (A) illustrates results from the QueenCharlotteFairweatherDenali transform zone (QCD) Panel (B) is the same for crustal seismicity in theAlaskanAleutian Trench (ATC) Panel (C) is the same for subcrustal seismicity of the AlaskanAleutianWadatiBenioff zone (ATD) Confidence limits of 95 are also drawn but are not always visible as they are usuallysmaller than the symbols

aftershock sequence Prior to 1989 the full nSAF subcatalogue contains 5738 events whileafter 1989 it has only 2862 It is interesting to observe (and certainly worthy of investigation)that during the first 20-year period the full nSAF catalogue contains almost twice the num-ber of events in comparison to the second 25-year period meaning that there are significantdifferences in productivity rates As is evident in Fig 1111A for the period 19681988qM(Mth) exhibits a quasilinear trend from 148 at Mth 5 3 to 156 at Mth 5 41 In Fig 1111Cthis trend has disappeared and qM(Mth) seems to have stabilized just below the value of 15However because the number of earthquakes available for analysis at Mth 37 is insuffi-cient it is not certain whether the lsquotrendrsquo has altogether disappeared or is simply unobserv-able The estimation of entropic indices with respect to interevent distance is limited toΔd 450 km due to the size of the SNR area Still one may observe that qM(Δd) is ratherstably determined for both periods slightly fluctuating about 15 (Fig 1111BD) Thetemporal entropic index however is very different For 196888 qT(Mth) it behavesexactly like its SCSR and SNR lsquosiblingsrsquo it displays an upward linear trend from 14 forMth 5 3 to higher than 16 for Mth 5 41 at an average rate of 017 per magnitude unitobtained by fitting a straight line to the data (Fig 1111A) This indicates strong

FIGURE 11ndash14 As per Fig 1113 but for qM versus interevent distance Δd binning schemes vary in order tomaximize statistical rigour

correlation and long-range interaction particularly at the larger threshold magnitudesSignificant strong correlation over all ranges is also observed in Fig 1111B whereqT(Δd) varies from 158 to 155 for Δd 100 km and consistently increases from 139 for100 km Δd 200 km to 166 for Δd 300 km Conversely for the period 19902015qT(Mth) is consistently lower than 115 so that hqT(Mth)i5 108 6 003 (Fig 1111C)Likewise qT(Δd) is consistently lower than 12 so that hqT(Δd)i5 113 6 0044(Fig 1111D) Thus after 1990 nSAF turns out to be practically Poissonian

The results above indicate that complexitycriticality may be dynamicevolutionary andnot stationary in the typical SOC sense The same results indicate that if criticality hadindeed been the cause of strong correlation in nSAF prior to 1989 it was probably not of thecritical point lsquovarietyrsquo because there has never been unequivocal observation of CP pointbehaviour (accelerating seismic release rates) leading to the Loma Prieta event It is alsohard to imagine that the dramatic reduction in correlation and seismicity rates after 1990 is

FIGURE 11ndash15 Analysis of the temporal entropic index qT versus threshold magnitude for the full (grey) anddeclustered (red) earthquake catalogues along the PacificNorth American plate boundary in Alaska and theAlaskanAleutian Arc (see Section 11312 and Fig 115 for details) Panel (A) illustrates results from the QueenCharlotteFairweatherDenali transform zone (QCD) Panel (B) is the same for crustal seismicity of theAlaskanAleutian Trench (ATC) Panel (C) is the same for subcrustal seismicity in the AlaskanAleutianWadatiBenioff zone Confidence limits of 95 are also drawn but are not always visible as they are usuallysmaller than the symbols

unrelated to aftereffects of the Loma Prieta event Accordingly such dynamic changes in thelevel of correlation may provide evidence as to how criticality develops waxes and wanestherefore as to the nature of a fault network in which criticality can develop Additionalmore detailed discussion on this subject is included in Section 115

11422 California Declustered CataloguesOne main objective of the present work is to investigate whether background seismicity isgenerated by non-Poissonian dynamic processes Therefore we examine declustered realiza-tions of the earthquake catalogues in which aftershock sequences have been removed bythe stochastic declustering method of Zhuang et al (2002) at the φ $ 70 probability levelIt is important to note that the populations of earthquakes available for analysis after declus-tering is not always sufficient to maintain statistical rigour in the estimation of entropic indi-ces Accordingly we reiterate that for the sake of experimental rigour analysis is notperformed for catalogue subsets containing less than 300 events and results are not consid-ered displayed and tabulated if associated with goodness of fits lower than or equal to 097The results are illustrated in Fig 1112 Specifically the variation of qM and qT with thresh-old magnitude Mth is shown in Figs 1112A and B respectively while the variation of qMand qT with interevent distance Δd is shown in Figs 1112C and D

In Fig 1112A it is apparent that qM(Mth) does not have any trait worth commenting onIt should be noted however that results could not be obtained for Mth 37 therefore it isnot known whether qM would behave as per the full catalogues It is also worth noting thaton removing dependent events the qM(Mth) determined for MFZ reduces to a mean value of151 6 0008 indicating that the high level of active fault clustering observed in the full cata-logue reduces to average levels

Turning to the analysis of temporal entropic indices in Fig 1112B we note that forSCSR qT(Mth) fluctuates smoothly and very stably around a mean value of 147 6 003which for the interval Mth A[25 37] is significantly higher than the mean value of 117 6

0083 observed in the full catalogue (Section 11421) This is clear evidence of significantlycorrelated background seismicity The same observation can be made in nSAF where qT

14 for Mth A [32 37] and hqT(Mth)i 5 145 6 007 as opposed to the mean value of 1256 0064 obtained for the full nSAF catalogue in the same magnitude interval This alsopoints to a significantly correlated background along the central and northern segments ofthe SAF despite the relaxation observed after the Loma Prieta event In SNR qT is character-ized by an increasing trend from 173 to 181 This also represents a surprisingly largeincrease in the level of correlation in background seismicity given also that the analysis ofthe full catalogue also indicated a strongly correlated seismogenetic process Finally andalmost opposite results described so far the declustered MFZ catalogue exhibits an upwardquasilinear trend from no correlation (qT 5 106) at Mth 5 30 to weak correlation hqTi 126 at Mth $ 36

Due to population statistics in the declustered catalogues determination of entropic indi-ces with respect to interevent distance could be reliably performed only for SCSR and MFZIn Fig 1112C it is apparent that qM(Δd) determinations for both catalogues are rather

unremarkable for SCSR they are very consistent so that hqM(Δd)i5 1497 6 0008(bq 5 101) while for MFZ they fluctuate rather significantly so that hqM(Δd)i5 1546 0022(bq 5 085) Interesting observations can be made with regard to the temporal entropic indexof the declustered SCSR catalogue only Here qT(Δd) varies from 165 for Δd 50 km to152 for Δd 150 km and between 142 and 152 for Δd $ 150 km The latter is also signifi-cantly and remarkably higher that the corresponding variation of qT in the full cataloguethereby confirming the existence of a significant to strong long-range correlation in SouthCalifornia Finally and presumably due to population statistics qT(Δd ) determinations fromthe declustered MFZ catalogue are limited to Δd 250 km they do not exhibit a patternand fluctuate around a mean value of 13 6 0153 possibly indicating a system weakly cor-related over short and intermediate ranges

The analysis so far so far has shown that on removing aftershock sequences significantlyhigher correlation is observed in comparison to the full catalogues In fact the SNR fault net-work exhibits such high correlation that any background earthquake anywhere in the systemwould appear to be able of influencing the occurrence of future events anywhere else Albeitto a lesser extent the same appears to hold true for the SCSR and nSAF fault systemsalthough a note of caution applies to the latter The MFZ catalogue has shown evidence ofsignificantly lower correlation in comparison to the other fault networks of California espe-cially at small cutoff magnitudes and short ranges Another interesting observation is therather higher values of the magnitude entropic index which may indicate increased cluster-ing of the fault network and lower crustal heterogeneity in that area

11423 North Pacific RimAs explained in Section 11312 we shall conduct a comparative analysis of the seismicityobserved along the two major components of the boundary between the North Americanand Pacific plates Queen CharlotteFairweather and Denali zone of transform faults(QCD) and the Aleutian Arc and Trench system (AT) formed by the northerly subductionof the Pacific plate under the North American plate The earthquakes caused by the formersystem occur primarily in the schizosphere The earthquakes caused by the latter occurboth in the crust and below the crust in association with the Aleutian WadatiBenioffzone In such a regional tectonic setting we take our enquiry one step further by attempt-ing to examine whether the environment in which seismogenesis occurs (pressure mate-rial homogeneity boundary conditions etc) has an effect on the dynamic expression ofthe seismogenetic system Accordingly we divide the Aleutian Arc and Trench seismicityinto crustal and subcrustal based on published estimates of the Mohorovicic discontinuityand conduct our analysis on two data subsets henceforth to be referred to as ATC (crustal seis-micity) and ATD (subcrustal seismicity) We examine the full catalogues as well as versions ofall catalogues declustered at the 70 level The results are summarily presented in Table 113and Figs 11131116

Fig 1113 illustrates the variation of the magnitude entropic index qM with respect tothreshold magnitude Focusing first on the QCD catalogues it is straightforward to observethat qM(Mth) is stable and exhibits minimal variation (Fig 1113A) However while the full

catalogue yields a mean value of 160 6 0008 (bq 5 067) the declustered catalogue yields152 6 0012 (bq 5 092) Analogous reduction of qM with declustering has also beenobserved in SCSR and MFZ (California) As in those activity in QCD is localized near thefault zones Therefore it is plausible that the reduction of qM levels between the full anddeclustered catalogues implies a corresponding reduction in the level of activity localizationfrom very high (bq 5 067) to nearly lsquoaveragersquo (bq 5 092) in direct consequence of clusteredaftershock removal This reduction appears to take place without the effects of scaling (hier-archical distribution) of the faults that does not change with magnitude

We now turn our attention to the results of the Aleutian Arc and Trench catalogues whichare shown in Figs 1113B and C For the full ATC catalogue qM(Mth) fluctuates slightlyabout the level of 15 so that hqM(Mth)i5 149 6 0016 (Fig 1113B) and bq(Mth) variesbetween 092 and 114 For the declustered ATC data qM(Mth) decreases smoothly fromapproximately 147 at Mth 5 44 to approximately 140 at Mth55 so that hqM(Mth)i5 144 6

003 (Fig 1113B) respectively bq(Mth) varies between 111 and 15 The small reduction of

FIGURE 11ndash16 As per Fig 1115 but for qT versus interevent distance Δd earthquakes are binned according toEq (1120) but binning schemes vary in order to maximize statistical rigour Error bars (not always visible) represent95 confidence limits

qM between the full and declustered catalogues might signify a corresponding reduction inthe level of activity localization as per QCD but to a lesser degree In Fig 1113C stable andmutually consistent determination of qM(Mth) is evident in both the full and declustered ATDcatalogues qM fluctuates slightly about 15 so that the mean qM is 151 6 0024 for the fullcatalogue and 149 6 001 for the declustered (no statistical difference)

Fig 1114 illustrates the variation of qM with respect to interevent distance qm(Δd)The results obtained from the QCD catalogues are shown in Fig 1114A It is straightfor-ward to see that both the full and declustered catalogues yield results analogous to thoseshown for qM(Mth) in Fig 1113A For the full catalogue qM(Δd) is rather consistent overinterevent distances of up to 800 km and varies between 156 and 161 so that hqT(Δd)i5158 6 002 respectively bq varies in the range 062077 In the declustered catalogueearthquake populations sufficient for statistically significant results exist only for intereventdistances between 150 and 450 km these yield very consistent qM(Δd) which varies between151 and 153 so that hqT(Δd)i5 152 6 0008 and bq(Δd) that varies between 094 and087 It is again possible to observe a statistically significant reduction in the value of qMwhich can be interpreted in terms of a corresponding reduction in the level of activity locali-zation upon aftershock removal Focusing next on the Aleutian Arch and Trench it is againstraightforward to observe that the full and declustered catalogues have yielded very consis-tent albeit unremarkable results In crustal seismicity (ATC) qM(Δd) is stable over intere-vent distances longer than 800 km and varies in the narrow range 148154 for both full anddeclustered catalogues so that bq varies between 108 and 085 (Fig 1114B) The same isobserved for subcrustal seismicity (ATD) qM(Δd) is stably determined over interevent dis-tances longer than 700 km and varies between 150 and 156 for both the full and declusteredcatalogues so that bq varies in between 10 and 079 (Fig 1114C)

The analysis of the temporal entropic index with respect to threshold magnitude is shownin Fig 1115 Starting again with QCD Fig 1115A illustrates the variation of qT(Mth) forthe full and declustered data sets It is apparent that the temporal entropic index starts offlow (qT B 11) but demonstrates a steady linear increasing trend For the full catalogue ittranscends the threshold of randomness at Mth 5 32 and climbs to 144 at Mth $ 45 (signifi-cant correlation) this variation can be fitted with a linear trend line giving an average rate of022 per magnitude unit For the declustered catalogue qT also transcends the randomnessthreshold at Mth 5 32 and climbs to 133 at Mth 5 38 the linear trend in this case has a rateof 029 per magnitude unit noticeably higher than that of the full catalogue It can also beseen that for all Mth 32 the declustered catalogue qT is consistently higher than the fullcatalogue qT indicating a more correlated background process It is also worth rememberingthat a quasilinear increase of qT with magnitude has been observed in the SCSR SNR andpre-1989 nSAF catalogues and has been attributed to operational long-range correlationtherefore the same interpretation should apply in the case of QCD

Focusing now on the Aleutian Arc and Trench Fig 1115B shows the variation ofqT(Mth) for the full and declustered datasets of crustal earthquakes (ATC) For the full cata-logue qT(Mth) varies around 12 so that hqT(Mth)i5 12 6 0054 However it can alsobe clearly seen that qT increases steadily from less than 115 at Mth544 to over 12 for

Mth $ 51 at an average rate 009 per unit magnitude (obtained by fitting a straight line tothe data) It is also clearly seen that in the declustered catalogue qT(Mth) increases steadilyfrom about 13 at Mth544 to over 14 for Mth $ 5 at a rate of 022 per magnitude unit andthe mean value also increases to 136 6 007 As before this implies a correlated back-ground and points toward a long-range interaction Exactly the opposite behaviour isobserved in subcrustal seismicity (ATD) As evident in Fig 1115C qT is generally lowerthan 11 and on the basis of this evidence alone the subcrustal fault network of the subduc-tion zone would appear to be Poissonian

In concluding the presentation of our results Fig 1116 demonstrates the analysis of thetemporal entropic index with respect to interevent distance Fig 1116A illustrates results fromthe transformational plate boundary (QCD) A rather unexpected outcome is that inadequateearthquake populations prohibited the generation of dependable estimation of qT at shortinterevent distances (less than 100150 km) even for the full catalogue it appears that evenaftershocks are rather broadly spread out along the QCD fault zones For interevent distanceslonger than 50 km the full-catalogue qT(Δd) is rather significant it always remains above thethreshold of randomness maximizing at ranges of the order 300 km to 600 km (qT 145) andthereafter slowly declining to moderate ranges of the order of 700 km (qT 13) Inadequatepopulations also did not allow determination of qT(Δd) from the declustered catalogue at dis-tances shorter than 150 km and longer than 450 km Yet within this range qT(Δd) is consistentlydetermined at the level 134137 indicating moderate correlation Given also the resultsobtained for qT(Mth) in Fig 1115A it can be concluded that within the period of observationthe QCD zone has existed in a persistent state of nonequilibrium

Switching now to the analysis of the crustal seismicity along the Aleutian Arc and Trenchwe note that for the full catalogue weak to moderate correlation can be observed only atinterevent distances shorter than 200 km and longer than 700 km in all other cases qT islower than the threshold of 12 (Fig 1116B) However declustering appears to unveil strongbackground correlation at long interevent ranges (300 km Δd 800 km) where qT variesbetween 145 and 165 and notably mirrors the variation of qT in the full catalogue(Fig 1116B) Unfortunately at short and intermediate ranges (Δd 300 km) qT cannot beestimated due to dwindled earthquake populations (and consequent loss of statistical robust-ness) The analysis of subcrustal seismicity (ATD) shows nihil to marginal correlation over allinterevent distances and up to 700 km (Fig 1116C) At the Mth544 level the full anddeclustered catalogues yield very comparable results For the former qT(Δd) determinationsvary between 132 and 109 with a mean of 119 6 007 and for the latter they vary between104 and 117 with a mean of 111 6 005 As per Fig 1115C subcrustal seismicity appearsto be Poissonian also with respect to interevent distance

115 DiscussionThe work reported herein begins with the question lsquoAre seismogenetic systems random ororganizedrsquo The question originates in a longstanding discourse between the two principal

schools of thought (and epistemological paradigms) developed in the process of studyingearthquake occurrence and quantifying the expectation of seismic activity Accordingly farfrom being purely academic the problem of understanding the proper statistical nature ofseismicity is also practical the answer can have significant repercussions on forecastingintermediate-term earthquake hazards

Seismicity comprises the superposition of a background process expressing the contin-uum of tectonic deformation and a foreground process of prolific short-term activity associ-ated with earthquake swarms orand aftershock sequences The first and historic school(doctrine) posits that background seismicity is produced by a self-excited conditionalPoisson (point) process whose entropy is assumed to obey the BoltzmannGibbs formalismbackground earthquakes are spontaneously and independently generated in the fault net-work and there is no interaction between faults such that would influence their time andplace of occurrence The second and more recent doctrine posits that background seismicityis generated by a nonequilibrating fault network (system) in which background events aredependent due to correlations (interactions) developing and evolving between faults whichmay extend over long spatiotemporal distances and influence their time and place of occur-rence Correlation effectively confers memory to the system and manifests itself in the formof power laws governing the temporal and spatial statistics of seismicity Both Poisson andcomplexcritical doctrines albeit from different vantage points consider the earthquakes ofan aftershock sequence to be dependent

Herein we attempt to explore the statistical nature of seismicity by using the generalizedformalism of NESP (described in Section 442) as a universal context for the statistical descrip-tion of earthquake occurrence and trying to ascertain the existence and degree of correlationin active fault networks (or equivalently the level of nonequilibrium) The existence of correla-tion is assessed by evaluating the entropic index q appearing in the q-exponential distributionpredicted by NESP for the dynamic parameters of nonequilibrating systems q is bounded as0 q 2 with q5 1 corresponding to the pure exponential distribution expected for con-servative Poissonian processes and q 1 indicating complexitycriticality in nonconservativesystems Specifically we evaluate an entropic index associated with the distribution of earth-quake magnitudes which conveys information about the size and space distribution of faultactivity and is genetically related to the b-value of the GutenbergRichter law and an entropicindex associated with the distribution of the lapse between consecutive events (intereventtime) which indicates the extent of interaction in a fault network We refer to these as themagnitude (qM) and temporal (qT) indices respectively and we compute them by modellingbivariate empirical distributions of earthquake frequency versus magnitude and intereventtime or FMT for short such distributions express the joint probability of observing earth-quakes larger than a given magnitude after a given lapse time

We examine seismogenetic systems along the NE and N boundary of the Pacific andNorth American plates Specifically we focus on the major transform fault systems ofCalifornia (south and north segments of the SAF SNR and MFZ) and Alaska (QueenCharlotteFairweather and Denali faults) as well as on the AlaskanAleutian convergenceWith reference to the latter we examine crustal and subcrustal earthquakes by separating

them according to the depth of the Mohorovicic discontinuity so as to inquire whether envi-

ronmental orand boundary conditions affect the dynamics of a fault network Finally we

apply our analysis to homogeneous and complete earthquake catalogues in which aftershock

sequences are either included (full catalogues) or removed (declustered catalogues) with the

efficient stochastic declustering method of Zhuang et al (2002) If background seismicity is

Poissonian the removal of aftershocks should reduce the earthquake catalogue to an uncor-

related set of events if it is does not the argument against Poissonian seismicity would be

compelling

Turning now to the discussion of our results it might be said that they comprise anlsquoexpectedrsquo part and an lsquointerestingrsquo part The lsquoexpectedrsquo part is the behaviour of the magni-tude entropic index qM which after conversion to a proxy b-value through Eq (1118) turnsout to be consistent with expectation from the GutenbergRichter law (see Tables 111 and112) Naturally qM exhibits differences between seismogenetic systems The analysis of fullcatalogues shows that some (eg MFZ Fig 119 QCD Figs 1113A and 1114A) exhibitrather high clustering of faulting activity and that in some cases (SCSR nSAF SNRFig 119A) the degree of clustering (qM) increases with threshold magnitude ie it extendsover long ranges At any rate in the general context of NESP the GutenbergRichter lawcan be almost naturally derived from the q-exponential distribution (Section 1122)Accordingly a most significant outcome of qM analysis is that it demonstrates that activefault networks may be classified as subextensive with a high degree of self-organization

Since there can be little doubt that the time-honoured frequencymagnitude distributionof Gutenberg and Richter emerges from nonextensive fault networks we shall concentratethe rest of our discussion on the temporal dynamics of seismicity as indicated by the tempo-ral entropic index qT Fig 1117A is a compact presentation and colour-coded classificationof all qT(Mth) functions shown in Figs 1110A 1111A 1111C 1112A 1112C and1115 and summarized in Tables 112 and 113 In the classification scheme all values ofqT(Mth) 115) as established in Section 1141 are shown in red and are considered to indi-cate nihil correlation Values higher than 115 generally indicate statistically significant corre-lation that is rated as weak (orange 115 qT 13) moderate (light green 13 qT

14) significant (green 14 qT 15) strong (light blue 15 qT 16) and very strong(blue 16 qT) The pie chart in Fig 1117B summarizes the proportions of qT classesdetermined from the full crustal catalogues and provides a succinct picture of the existenceextent and relative strength of correlation in the crustal seismogenetic systems we have stud-ied Note that for nSAF the results used in the compilation of the pie chart refer to the entireperiod 19682015 the results obtained from the analysis of the ante and post Loma Prietasubcatalogues have not been included separately The pie chart in Fig 1117C is as perFig 1117B but for the declustered crustal catalogues Finally Fig 1117D as perFig 1117A is a compact presentation and colour-coded classification of all qT(Δd) func-tions from the full and declustered earthquake catalogues analysed herein and shown inFigs 1110B 1111B 1111D 1112B 1112D and 1116 In this case however the redclass of Poissonian processes spans the interval 1 qT(Δd) 12 as has been establishedin Section 1141

FIGURE 11ndash17 (A) Summarization and classification of all qT versus Mth determinations shown in Figs 1110A1111A 1111C 1112A 1112C and 1115 (B) Proportions of qT(Mth) classes determined from the analysis offull crustal catalogues nSAF results used in the compilation of the pie chart refer to the entire period 19682015ATD results are excluded (C) Proportions of qT(Mth) classes determined from the analysis of declustered crustalcatalogues ATD is again excluded (D) Summarization and classification of all qT versus Δd determinations shownin Figs 1110B 1111B 1111D 1112B 1112D and 1116

Mere inspection of Fig 1117 should suffice to satisfy one that the crustal seismogeneticsystems we have studied are clearly correlated (Fig 1117B) and that correlation increaseswith declustering (Fig 1117C) This is a point of significance in that it demonstrates thatremoval of the clutter effected by the large numbers of time-local foreground eventsuncovers the existence of long-range interaction in the global background process Theextent of long-range correlation can be explicitly studied in Fig 1117D where it becomesapparent that at intermediate and long interevent distances (Δd 150 km) crustal seismic-ity is generally correlated and that declustering either does not affect the degree of correla-tion or causes it to increase It should also be noted that even with full (clustered)earthquake catalogues the correlation observed at interevent distances longer than 200 kmcan hardly be explained in terms of aftershock sequences such ranges are several timeslarger than the characteristic dimensions of aftershock zones associated with Mw 667earthquakes and significantly longer than zones associated with Mw 772 earthquakes (egKagan 2002) Therefore it is more reasonable to argue in favour of an operational long-range interaction Significant to strong correlation is generally observed at short intereventdistances (Δd 100 km) a result easily explained by the overwhelming effect of (clusteredand correlated) aftershock sequences

Another point of significance is that ETAS-based stochastic declustering fails to reduceearthquake catalogues to sequences of independent events van Stiphout et al (2012) pre-sented a study in which they compared declustering algorithms by applying the χ2

goodness-of-fit test to determine whether the lsquobackgroundrsquo recovered by some declusteringalgorithm obeys a Poisson distribution in time They found at the 5 significance level thatcatalogues declustered by the methods of Zhuang et al (2002) and Marsan and Lenglineacute(2008) follow a Poisson distribution in time accordingly they suggest that Poisson processesare in control of the background seismicity We contend that this (and analogous) tests maybe misleading because the distribution of occurrence times is not a measure of the interrela-tionship between distal successive events whatsoever and does not relate the occurrence ofan earthquake to its predecessor and successor events On the other hand the distributionof interevent times does as adequately explained in the foregoing

Based on our results and as far as crustal seismogenetic systems are concerned theanswer to the question asked in the title appears to be that they are complex and that theircomplexity involves a long-range interaction therefore they are organized However thereare different mechanisms by which complexity may arise Inasmuch as power-law distribu-tions and long-range effects are hallmarks of critical phenomena SOC and self-organizingcriticality (which naturally emerge from the inherent nonlinear dynamics of the fault system)are by far the principal candidates However complexity and criticality do not always gohand in hand and there are noncritical mechanisms that may generate power-laws (egSornette 2004 Sornette and Werner 2009) In one such example Celikoglu et al (2010)demonstrated that the CNM can generate q-exponential distributions of interevent timesalthough their simulation was incomplete in the sense that it did not include some spatial(geometric) configuration of interacting faults and could not assess the differences with anactual fault network

Our results may be of some use in the course of understanding the origin and nature ofcomplexity in the fault systems we have studied First let us make a list of some points weconsider significant

bull To begin with different fault systems may exhibit radically different attributes anddegrees of complexity A characteristic example is the adjacentrelated MFZ and SNRsystems in which the first is partly accommodating the deformation effected by thesecond However the former exhibits marginal correlation while the second is strong orvery strong This may indicate that models calling for external driving forces that act uponall elements of a fault system as for instance the CNM may not be applicable to regionalscales

bull Some systems (SCSR SNR QCD and ATC) exhibit persistent and significant to stronglong-range correlation over the entire period of 47 years since 1968 this reveals itselfboth implicitly as an increase in the value of qT with threshold magnitude (Fig 1117A)and explicitly (Fig 1117D) it is also observable in the full and declustered cataloguesFar from suggesting that the state of strong correlation may endure lsquoforeverrsquo we shouldnevertheless point out that this quasistationary state of high correlation has attributes ofSOC

bull SOC is not a general rule This is evident in nSAF undergoing enormous changes withrespect to the large Loma Prieta earthquake and switching from a state of high correlationwith strong attributes of criticality during the period leading up to the event (196889) topractically Poissonian in the period following the event (1990present) This shows thatcriticality may be cyclic and possibly evolving in association with earthquake cycles Italso has attributes of the self-organizing variety albeit without evident acceleration ofseismic release rates as predicted by some models (eg Sammis and Sornette 2001) It isvery possible that the plain designation lsquocriticalityrsquo would suffice to characterize this caseor maybe all cases for that matter

bull A last important point we would like to make is that the only subcrustal system studiedherein the AlaskanAleutian subduction (ATD) is definitely Poissonian Because it is theonly one it cannot serve as a basis for generalizations The contrast with the crustalsystems is rather impressive nonetheless and may comprise a piece of information usefulin the course of shaping up some preliminary understanding of the statistical (andphysical) nature of seismogenesis As seen in Fig 116C the GutenbergRichter a-valueis lsquonormalrsquo and the b-value is almost the same as the global average Moreover as seen inFig 1113D and Table 113 qM varies from 153 (bq5088) at Mth544 to 146 (bq5116)at Mth552 with a tendency to decrease (increase) On the other hand there is (meagre atpresent) an indication that some crustal systems exhibiting strong long-range correlationalso exhibit long-range activity localization therefore large-scale crustal homogeneity forinstance in SCSR nSAF and SNR and for Mth 35 the full-catalogue qM increasessteadily with threshold magnitude from approximately 15 (bq51) to approximately 158(bq5072) while it remains persistently high in QCD (qM B16 bq B067) While thebehaviour of qM is clearly different between ATD and the crustal systems possibly

indicating different dynamics gross earthquake productivity rates and large-scale domainheterogeneities are not dramatically dissimilar Accordingly the absence of temporalcorrelation in ATD may not have to do with the material properties of the subducting slaband should be sought elsewhere

Based on the above observations we believe that we can put together the basics of a plau-sible interpretation for our results which will be based on fault networks with small-worldtopologies (eg Abe and Suzuki 2004 2007 Caruso et al 2005 2007) Given that active faultnetworks are nonconservative systems friction is a nonconservative force and thereforesusceptible to nonlinearity we are pointed to this direction by the documented existence oflong-range interaction and (possible) criticality fruitful studies based on nonconservativesmall-world OlamiFederChristensen models (Caruso et al 2005 2007) and suggestive evi-dence of small-worldness in the seismicity of California by Abe and Suzuki (2004 2007)

In such networks each fault is a node that belongs to a local cluster where it occupiessome hierarchical level according to its size and interacts with local or distal faults (nodes)according to the respective connectivity and range of its hierarchical level Upon excitationby some (slow or fast) stress perturbation a node responds by storing (accumulating) energyin the form of strain and subsequently transmitting it to connected nodes orand releasing itat various rates in other words it operates as a delayed feedback loop inducing heterogene-ity in the distribution of stress transfer and release rates Finally and more importantlycrustal fault networks are subject to free boundary conditions at the Earthatmosphereinterface top-tier faults (which in transformational and convergent tectonic settings generallybreak at the surface) comprise primary boundary elements of the network

It is documented that in OlamiFederChristensen networks free boundary conditionscompel the boundary elements to interact at a different (delayed) frequency with respect tothe bulk of elements buried deeper in the network and that this inhomogeneity induces partialsynchronization of the boundary elements building up long-range spatial correlations andfacilitating the development of a critical state (eg Lise and Paczuski 2002 Caruso et al 2005Hergarten and Krenn 2011) This effect should also be accentuated by heterogeneity anddelayed feedback across the entire network which also appear to be important for the devel-opment of criticality in small-world networks (Yang 2001 Caruso et al 2007) In the particu-larly interesting study by Hergarten and Krenn (2011) the dynamics of the network aregoverned by two competing mechanisms synchronization which pushes the system towardcriticality and desynchronization which prevents it from becoming overcritical and generatesforeshocks and aftershocks Once the system has reached the critical state synchronized failuretransfers more stress to connected nodes and this causes them to fail early desynchronizingwith the rest of the system If however the time lag between desynchronized failures is shortthe system can resynchronize and repeat the cycle This mechanism generates sequences offoreshocks mainshocks and aftershocks Notably the notion that aftershocks are generated bythe desynchronization caused by large earthquakes is quite different and more SOC thanthat of spontaneous triggering advocated by the ETAS model

In consequence of the above it is plausible that the small-world character and subexten-sive critical state of crustal fault networks along the boundary of the Pacific and North

American plates is induced by the high connectivity of synchronized top-tier faults forinstance the contiguous segments of the large transform faults These may operate as lsquohubsrsquothat facilitate longitudinal interactions (transfer of stress) between distal clusters but inhibitinteractions between distal or unconnected networks that operate quasiindependently anddevelop different levels of self-organization as for instance between nSAF and SNR or nSAFand MFZ In addition the intensity of the longitudinal interactions may vary in response totime-dependent changes in the external driving force and connectivity (stress transfer)between hubs as for instance may have happened to nSAF before and after the Loma Prietaevent The interpretation posits that free boundary conditions are central to the developmentof complexity and criticality By inference it also implies that deep-seated fault networks asfor instance those of WadatiBenioff zones should be kept away from criticality as they aresubject to fixed boundary conditions that inhibit synchronization If this holds water it mightbe the primary reason why subcrustal seismicity in the AlaskanAleutian subduction isPoissonian Nevertheless as we have already stated before rigorous inferences and generali-zations cannot be based on only one example It follows that this detail and the whole inter-pretation of our analysis for that matter remains to be tested with future research

In a final comment we note that our analysis has been based on statistical physics forwhich the designation lsquostatisticalrsquo may not have the same meaning as that in lsquostatistical seis-mologyrsquo As eloquently pointed out by Sornette and Werner (2009) statistical seismology islsquoa field that has developed as a marriage between probability theory statistics and the part ofseismology concerned with empirical patterns of earthquake occurrences but not withphysicsrsquo On the other hand statistical physics endeavours to generate the statistical modelsfrom first principles respecting the laws of thermodynamics and taking into account physicallaws such as those of friction rupture etc In other words it uses physics to support stochas-tic models a quality often missing from traditional statistical seismology (Dieterich 1994) Inthis respect our NESP-based approach is constrained by physics and as such it is analo-gously significant

AcknowledgementsThis work was partly supported by the THALES Program of the Ministry of Education of Greece and theEuropean Union in the framework of the project lsquoIntegrated understanding of Seismicity using innovativemethodologies of Fracture Mechanics along with Earthquake and Nonextensive Statistical Physics Application to the geodynamic system of the Hellenic Arc SEISMO FEAR HELLARCrsquo

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Wesnousky S 2005 Active faulting in the Walker Lane Tectonics 24 (3) TC3009 Available from httpsdoiorg1010292004TC001645

Yang XS 2001 Chaos in small-world networks Phys Rev E 63 046206 Available from httpsdoiorg101103PhysRevE63046206

Yeats R 2013 Active Faults of the World Cambridge University Press Cambridge

Zhuang J Ogata Y Vere-Jones D 2002 Stochastic declustering of space-time earthquake occurrencesJ Amer Stat Assoc 97 369380

Intro_Pages from Chelidze-CSTS-1631280-3
Tzanis_et_al_2018_in_Chelidze-CSTS-1631280-2

Page 2: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

11Are Seismogenetic Systems Randomor Organized A Treatise of TheirStatistical Nature Based on theSeismicity of the North-NortheastPacific Rim

Andreas Tzanis1 Angeliki Efstathiou1 Filippos Vallianatos21NATIONAL AND KAPODISTRIAN UNIVERSITY OF ATHENS ATHENS GREECE

2TECHNOLOGICAL EDUCATIONAL INSTITUTE OF CRETE AND UNESCO CHAIR ON SOLIDEARTH PHYSICS AND GEOHAZARDS RISK REDUCTION CHANIA CRETE GREECE

CHAPTER OUTLINE

111 Introduction 366

112 Nonextensive Approach to the Statistical Physics of Earthquakes 370

1121 Brief Exposeacute of NESP 370

1122 Seismicity and NESP An Overview 373

1123 Multivariate Earthquake Frequency Distributions Construction andNESP-Based Modelling 375

113 Earthquake Data and Analysis 379

1131 Earthquake Source Areas and Catalogues 379

1132 California 379

1133 Declustering 388

114 Results 390

1141 Determination of Randomness Thresholds 391

1142 Entropic Indices 392

115 Discussion 405

Acknowledgements 412

References 412

SqethpTHORN5k

q2 112

ethN0

pqethxTHORNdx

eth111THORN

hxiq 5ethN0

where

pethxTHORN5 1

Zqexpq 2

λIq

x2 hxiq

eth115THORN

Zq 5

ethN0

expq 2λIqU x2 hxiq

dx

Iq 5ethN0

frac12pethxTHORNqdx

8lt eth116THORN

xc 5x0

ETHN0 expq x0=x0

dx0

eth118THORN

Peth xTHORN5ethNx

5 12eth12qTHORN x

x0

112q

eth1110THORN

σ2hσiq 1

12q

eth1111THORN

pethETHORN5 2

3UE21=3

α2=3

U 12

E2=3

α2=3

2 112q

5 1212qM22qM

U102M

α2=3

22qM12qM

13 eth1112THORN

5 1212qM22qM

U10M

α2=3

22qM12qM

13 eth1113THORN

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 3: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

SqethpTHORN5k

q2 112

ethN0

pqethxTHORNdx

eth111THORN

hxiq 5ethN0

where

pethxTHORN5 1

Zqexpq 2

λIq

x2 hxiq

eth115THORN

Zq 5

ethN0

expq 2λIqU x2 hxiq

dx

Iq 5ethN0

frac12pethxTHORNqdx

8lt eth116THORN

xc 5x0

ETHN0 expq x0=x0

dx0

eth118THORN

Peth xTHORN5ethNx

5 12eth12qTHORN x

x0

112q

eth1110THORN

σ2hσiq 1

12q

eth1111THORN

pethETHORN5 2

3UE21=3

α2=3

U 12

E2=3

α2=3

2 112q

5 1212qM22qM

U102M

α2=3

22qM12qM

13 eth1112THORN

5 1212qM22qM

U10M

α2=3

22qM12qM

13 eth1113THORN

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 4: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

SqethpTHORN5k

q2 112

ethN0

pqethxTHORNdx

eth111THORN

hxiq 5ethN0

where

pethxTHORN5 1

Zqexpq 2

λIq

x2 hxiq

eth115THORN

Zq 5

ethN0

expq 2λIqU x2 hxiq

dx

Iq 5ethN0

frac12pethxTHORNqdx

8lt eth116THORN

xc 5x0

ETHN0 expq x0=x0

dx0

eth118THORN

Peth xTHORN5ethNx

5 12eth12qTHORN x

x0

112q

eth1110THORN

σ2hσiq 1

12q

eth1111THORN

pethETHORN5 2

3UE21=3

α2=3

U 12

E2=3

α2=3

2 112q

5 1212qM22qM

U102M

α2=3

22qM12qM

13 eth1112THORN

5 1212qM22qM

U10M

α2=3

22qM12qM

13 eth1113THORN

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 5: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

SqethpTHORN5k

q2 112

ethN0

pqethxTHORNdx

eth111THORN

hxiq 5ethN0

where

pethxTHORN5 1

Zqexpq 2

λIq

x2 hxiq

eth115THORN

Zq 5

ethN0

expq 2λIqU x2 hxiq

dx

Iq 5ethN0

frac12pethxTHORNqdx

8lt eth116THORN

xc 5x0

ETHN0 expq x0=x0

dx0

eth118THORN

Peth xTHORN5ethNx

5 12eth12qTHORN x

x0

112q

eth1110THORN

σ2hσiq 1

12q

eth1111THORN

pethETHORN5 2

3UE21=3

α2=3

U 12

E2=3

α2=3

2 112q

5 1212qM22qM

U102M

α2=3

22qM12qM

13 eth1112THORN

5 1212qM22qM

U10M

α2=3

22qM12qM

13 eth1113THORN

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 6: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

SqethpTHORN5k

q2 112

ethN0

pqethxTHORNdx

eth111THORN

hxiq 5ethN0

where

pethxTHORN5 1

Zqexpq 2

λIq

x2 hxiq

eth115THORN

Zq 5

ethN0

expq 2λIqU x2 hxiq

dx

Iq 5ethN0

frac12pethxTHORNqdx

8lt eth116THORN

xc 5x0

ETHN0 expq x0=x0

dx0

eth118THORN

Peth xTHORN5ethNx

5 12eth12qTHORN x

x0

112q

eth1110THORN

σ2hσiq 1

12q

eth1111THORN

pethETHORN5 2

3UE21=3

α2=3

U 12

E2=3

α2=3

2 112q

5 1212qM22qM

U102M

α2=3

22qM12qM

13 eth1112THORN

5 1212qM22qM

U10M

α2=3

22qM12qM

13 eth1113THORN

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 7: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

SqethpTHORN5k

q2 112

ethN0

pqethxTHORNdx

eth111THORN

hxiq 5ethN0

where

pethxTHORN5 1

Zqexpq 2

λIq

x2 hxiq

eth115THORN

Zq 5

ethN0

expq 2λIqU x2 hxiq

dx

Iq 5ethN0

frac12pethxTHORNqdx

8lt eth116THORN

xc 5x0

ETHN0 expq x0=x0

dx0

eth118THORN

Peth xTHORN5ethNx

5 12eth12qTHORN x

x0

112q

eth1110THORN

σ2hσiq 1

12q

eth1111THORN

pethETHORN5 2

3UE21=3

α2=3

U 12

E2=3

α2=3

2 112q

5 1212qM22qM

U102M

α2=3

22qM12qM

13 eth1112THORN

5 1212qM22qM

U10M

α2=3

22qM12qM

13 eth1113THORN

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 8: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

SqethpTHORN5k

q2 112

ethN0

pqethxTHORNdx

eth111THORN

hxiq 5ethN0

where

pethxTHORN5 1

Zqexpq 2

λIq

x2 hxiq

eth115THORN

Zq 5

ethN0

expq 2λIqU x2 hxiq

dx

Iq 5ethN0

frac12pethxTHORNqdx

8lt eth116THORN

xc 5x0

ETHN0 expq x0=x0

dx0

eth118THORN

Peth xTHORN5ethNx

5 12eth12qTHORN x

x0

112q

eth1110THORN

σ2hσiq 1

12q

eth1111THORN

pethETHORN5 2

3UE21=3

α2=3

U 12

E2=3

α2=3

2 112q

5 1212qM22qM

U102M

α2=3

22qM12qM

13 eth1112THORN

5 1212qM22qM

U10M

α2=3

22qM12qM

13 eth1113THORN

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 9: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

Zq 5

ethN0

expq 2λIqU x2 hxiq

dx

Iq 5ethN0

frac12pethxTHORNqdx

8lt eth116THORN

xc 5x0

ETHN0 expq x0=x0

dx0

eth118THORN

Peth xTHORN5ethNx

5 12eth12qTHORN x

x0

112q

eth1110THORN

σ2hσiq 1

12q

eth1111THORN

pethETHORN5 2

3UE21=3

α2=3

U 12

E2=3

α2=3

2 112q

5 1212qM22qM

U102M

α2=3

22qM12qM

13 eth1112THORN

5 1212qM22qM

U10M

α2=3

22qM12qM

13 eth1113THORN

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 10: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

σ2hσiq 1

12q

eth1111THORN

pethETHORN5 2

3UE21=3

α2=3

U 12

E2=3

α2=3

2 112q

5 1212qM22qM

U102M

α2=3

22qM12qM

13 eth1112THORN

5 1212qM22qM

U10M

α2=3

22qM12qM

13 eth1113THORN

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 11: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

σ2hσiq 1

12q

eth1111THORN

pethETHORN5 2

3UE21=3

α2=3

U 12

E2=3

α2=3

2 112q

5 1212qM22qM

U102M

α2=3

22qM12qM

13 eth1112THORN

5 1212qM22qM

U10M

α2=3

22qM12qM

13 eth1113THORN

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 12: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

Nmτ 5Xτj5DT

Xmi5DM

Hij3Hij 6frac14 0

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 13: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

5 1212qM22qM

U10M

α2=3

22qM12qM

ΔtΔt0

1

12qT eth1116THORN

Pτj5DT

Pmi5DM

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 14: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

5 a122 qM12 qM

0

1A log 12

12 qM22 qM

10M

α2=3

0

1A1

1

12 qTlog 12Δt21

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 15: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 16: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

1132 California

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 17: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 18: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 19: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 20: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 21: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

Full Catalogues

Mcomp

NoEvents Mcomp

NoEvents

SCSR SCSN 19802015 26 20088 26 3339

nSAF NCSN 19682015 30 8596 32 943

SNR NCSN 19682015 30 4982 32 591

QCD AEC 19682015 30 4332 30 1639

AleutianArc

Crustalearthquakes

ATCAEC 19682015 44

4775 44 1608

ATD 1720 44 1381

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 22: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 23: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 24: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 25: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

1133 Declustering

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 26: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

pj 5Xj21

i51

pij

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 27: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 28: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 29: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70

compelling

2113

Page 30: Complexity of Seismicusers.uoa.gr/~atzanis/Papers/Are_Seismogenetic... · Measurement and Application Edited by Tamaz Chelidze M. Nodia Institute of Geophysics, Tbilisi, Georgia Filippos

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)RANGE

qM(Δd)Range

bq(Δd)Range

Declusteredφ $ 70

SCSR 151142 164142 142152 151153 096089 149151 108096nSAF 148123 151152 096092 SNR 156181 151155 096082 MFZ 106142 113142 110138 149152 092104 151156 096079

qT(Mth)Range

qT (Δd) Range

qM(Mth)Range

bq(Mth)Range

qM(Δd)Range

bq(Δd)Range

DECLUSTEREDφ $ 70