LETTERSPUBLISHED ONLINE: 3 OCTOBER 2016 | DOI: 10.1038/NGEO2817
Triggering of the 2014Mw7.3 Papanoa earthquakeby a slow slip event in Guerrero, MexicoM. Radiguet1*, H. Perfettini1, N. Cotte1, A. Gualandi2,3†, B. Valette4, V. Kostoglodov5, T. Lhomme1,A. Walpersdorf1, E. Cabral Cano6 and M. Campillo1
Since their discovery two decades ago1,2, slow slip events havebeen shown to play an important role in accommodating strainin subduction zones. However, the physical mechanisms thatgenerate slow slip and the relationships with earthquakes areunclear. Slow slip events have been recorded in the Guerrerosegment of the Cocos–North America subduction zone2,3. Herewe use inversion of position time series recorded by a con-tinuous GPS network to reconstruct the evolution of aseismicslip on the subduction interface of the Guerrero segment.We find that a slow slip event began in February 2014, twomonths before the magnitude (Mw) 7.3 Papanoa earthquakeon 18 April. The slow slip event initiated in a region adjacentto the earthquake hypocentre and extended into the vicinity ofthe seismogenic zone. This spatio-temporal proximity stronglysuggests that the Papanoa earthquake was triggered by theongoing slow slip event. We demonstrate that the triggeringmechanism could be either static stress increases in thehypocentral region, as revealed by Coulomb stress modelling,or enhanced weakening of the earthquake hypocentral areaby the slow slip. We also show that the plate interface in theGuerrero area is highly coupled between slow slip events, andthat most of the accumulated strain is released aseismicallyduring the slow slip episodes.
Along the plate interface of subduction megathrust, at depthshallower than 40 km, seismic and aseismic processes coexist andcomplement each other in space and time. Slow slip events (SSEs)are a recently discovered1,2 form of aseismic slip that has modifiedour understanding of the accommodation of plate motion alongfaults. SSEs are transient episodes of aseismic slip lasting fromseveral days to several months, with a released seismic momentsometimes equivalent to large earthquakes4 (Mw > 7.5). SSEs andlarge earthquakes often occur on adjacent regions of the faultinterface, and for this reason, understanding the interaction betweenthese two phenomena is crucial for earthquake hazard assessment.Until now, different types of interaction have been proposed.Some studies5–8 suggest that SSEs may be the triggering factorof large megathrust earthquakes. There is also observational9,10and theoretical evidence11 that local or regional earthquakes cantrigger SSEs and associated tremors signals, or modify an ongoingSSE12. Association between SSEs and seismicity swarms has alsobeen observed13. In this paper, we show clear evidence that alarge subduction thrust earthquake in the Guerrero segment was
triggered by an ongoing SSE. We also evaluate the impact ofthe SSEs on the strain accumulation in the Guerrero subductionzone region.
The Guerrero segment of the Mexican subduction zone (Fig. 1a)is exceptionally interesting, with the largest known SSEs (equivalentto∼Mw7.5) occurring approximately every four years2,3. The timeseries of the first GPS (Global Positioning System) station installed(CAYA, Fig. 1b) in the area show five events detected in 1998,2001–2002, 2006, 2009–2010 and 2014. Previous studies3,14 revealedthat slow slip in that region occurs at depth between 15 and 40 km,which is shallower than SSEs observed in most of the subductionzones, where they are usually identified in the transition zone belowthe seismogenic one, at depth larger than 30 km (ref. 4). So far inGuerrero, an apparent increase of seismicity during SSE periods hasbeen observed15, but no correlation with the occurrence of largemegathrust earthquakes has been previously identified. Associatedwith the SSE activity, an increase in tectonic tremors16 and low-frequency-earthquake activity17 has been detected. Furthermore, apossible dynamic triggering of tremors and SSE in Guerrero by the2010Mw8.8Maule earthquake has been suggested9. In this paper, weinvestigate the 2014 SSE and its relation to the 18 April 2014Mw7.3Papanoa earthquake that ruptured a segment of the Pacific trenchnorthwest of the Guerrero seismic gap (Fig. 1a).
We used data from39 continuousGPS (cGPS) stations inMexico.The time series of the north–south component reveal the alternationof loading periods, with northward motion, referred to as ‘inter-SSE’ periods, and episodes of slow slip with southward motion(see Fig. 1b). To separate the SSE signal from the continuousinter-SSE loading, we remove the linear inter-SSE trend fromthe GPS time series, following the method previously developedin ref. 3 (see Methods). The time series in Fig. 1c present thelatest 2014 SSE that initiated in February 2014 and lasted untilDecember 2014. Clearly, some time series are also affected bythe Papanoa earthquake, which produced significant (>5mm) co-seismic offsets at six GPS stations of the network (ARIG, IGUA,MEZC, PAPA, TCPN and ZIHP), the largest offset of 120mmin horizontal displacement being observed at station ZIHP. Toestimate the aseismic contribution, we removed the co-seismicoffsets from the time series for the stations that are affected by theearthquake. The estimated co-seismic offsets are then inverted toinfer the co-seismic slip of the Papanoa earthquake (blue contourin Fig. 2a and Supplementary Fig. 5). The obtained GPS-based
1Université Grenoble Alpes, CNRS, IRD, ISTerre, F-38000 Grenoble, France. 2Settore di Geofisica, Dipartimento di Fisica e Astronomia, Università diBologna, 40127 Bologna, Italy. 3Istituto Nazionale di Geofisica e Vulcanologia (INGV), Section of Bologna, 40128 Bologna, Italy. 4Université de SavoieMont-Blanc, IRD, ISTerre, 73376 Le Bourget du Lac, France. 5Instituto de Geofísica, Universidad Nacional Autónoma de México, CP 04510, Ciudad deMéxico, México. 6Departamento de Geomagnetismo y Exploración, Instituto de Geofísica, Universidad Nacional Autónoma de México, CP 04510, Ciudadde México, México. †Present address: California Institute of Technology, Department of Geology and Planetary Sciences, Pasadena, California 91125, USA.*e-mail: [email protected]
Figure 1 | Map of the Guerrero area and examples of GPS time series. a, GPS station location (triangles) and significant earthquake epicentres (stars, fromref. 18 and USGS catalogue) are indicated. Grey arrows, PVEL model30 of relative plate motion (mm yr−1); thin black lines, isodepth contours (in km) of thesubducted oceanic slab22,23; northwestern Guerrero seismic gap25 (G. gap), red line at the trench. b, North GPS time series for the CAYA station, revealingfive large SSEs (highlighted by yellow bands). c, North time series (not detrended) at four representative stations showing the 2014 SSE and the co-seismico�sets of the Papanoa earthquake (truncated for clarity at station ZIHP) and its main aftershock (vertical dashed lines).
slip model is consistent, although smoother, with a previouslyproposed slip model based on seismological data18 (SupplementaryFig. 5). The largest aftershock of the Papanoa earthquake (Mw6.4 on8 May) also produced significant co-seismic offsets at three stations(PAPA, TCPN and ARIG), and the corresponding time series havebeen corrected consequently. The detrended and co-seismic-offset-corrected time series between October 2013 and December 2014are then inverted to study the spatio-temporal evolution of theaseismic signal. We use the principal component analysis inversionmethod (PCAIM) software19,20 to decompose and invert the GPStime series, using a spatial smoothing operator described in ref. 21.Three principal components are sufficient to explain the data(Supplementary Fig. 2). The inversion procedure is based on a least-square formulation3, and further details on parameter choices aregiven in the Methods. In the case of the SSEs occurring in Guerrero,we use a three-dimensional slab geometry based on the flat slabcontours proposed in ref. 22, and adjusted in the central section tothe receiver-function profile in ref. 23. Alternatively, we also testedthe Slab1.0 geometry24, which does not show the flat subductionpart in the central Guerrero region for depth larger than 40 km.At the depth of interest for this study, the two models produceequivalent results (Supplementary Figs 9 and 11). In Guerrero, theshort trench to coast distance (around 60 km) and the relativelyshallow location of SSEs ensure a good resolution of the inversiondue to the unprecedented closeness between the SSE area and thecGPS network location (Supplementary Fig. 8).
The slip distribution obtained by inverting the 20 cGPS timeseries is shown in Fig. 2a. Details of full spatio-temporal evolutionare displayed in Supplementary Fig. 6, and fitted to GPS time series
in Supplementary Fig. 7. The equivalent magnitude of this SSE isMw7.6. The time evolution of the moment release (Fig. 2c) revealsthat the Papanoa earthquake occurred in the early stage of the SSE(∼80 days after initiation), with about 15% of the SSE momentreleased before 18 April, corresponding to an aseismic momentrelease by the ongoing SSE equivalent to aMw7.1 earthquake (Fig. 2a,upper right). To assess the stressmodulation induced by the ongoingSSE on the Papanoa earthquake epicentral region, we computed theCoulomb failure stress change 1CFS on the fault plane estimatedfrom the pre-earthquake aseismic slip (upper right snapshot inFig. 2a). Due to the tendency of the inversion to smear out theslip distribution, we compute 1CFS for areas with significant slip(>50% of maximum slip, green dashed contour in Fig. 2a, upperright) thereby concentrating the slip distribution. We estimated1CFS of about 40 kPa in the hypocentral area (Fig. 2b), which isadjacent to the region of significant slip of the SSE. Whereas forprevious events (see slip contours in Fig. 4 and ref. 3) the maximumslip (∼0.15m) was located in the Guerrero gap (∼100◦W), the2014 SSE presents a larger maximum slip (∼0.25m) that is locatedat a longitude of 101◦W, downdip from the Papanoa earthquakerupture area, which probably results, at least partially, from afterslipof this earthquake.
The frictional properties of this subduction interface can becaptured by analysing the evolution of its degree of locking. Duringinter-SSE periods, we evaluate the short-term displacements rates,which are nearly constant during each successive inter-SSE period(red line in Fig. 3a and Supplementary Figs 3 and 4a). The mapof the short-term coupling (Fig. 3b), obtained by static (that is, noPCA decomposition is performed) inversion of these displacements
Figure 2 | Spatio-temporal evolution of the 2014 Guerrero SSE and interactions with theMw7.3 Papanoa earthquake. a, Cumulative slip distribution forthe 2014 SSE (left panel), and snapshots of the aseismic slip prior to the Mw7.3 earthquake (top-right panel), and between the Mw7.3 earthquake and itsmain aftershock Mw6.4 (bottom-right panel). Note the change in the colour scale. Blue line, Papanoa earthquake 0.15 m slip contour (see SupplementaryFig. 5); green dashed lines, 50% of maximum slip contour; stars, earthquake epicentres (see Fig. 1a). b, Coulomb failure stress change on the subductioninterface, computed from the slip distribution inside the green contours in top-right panel in a. c, Moment released during the 2014 SSE (blue dots: raw andred line: smoothed). Dashed vertical lines, earthquake occurrence.
rates, reveals that the area where SSEs occur (green contour for the2014 SSE) is strongly locked between the slow slip episodes, with alocking degree similar to the areas of major historical earthquakes(light blue contours). Note also the absence of shallow couplingin the Guerrero gap region, which is constrained mainly by thelower vertical subsidence of the GPS coastal stations in the area(Supplementary Figs 3 and 4a). We also study the evolution ofcoupling over a longer timescale, using the long-term displacementrates (blue line in Fig. 3a and Supplementary Figs 3 and 4b),which integrate both the inter-SSE locking and the slow slip motion(see Methods and Supplementary Fig. 3), and can be seen as aproxy for deformation over decades. The long-term coupling mapreveals a low coupling in the SSE region (<0.25) proving that most(around 75%) of the convergent plate motion is accommodatedaseismically in that region. Overall, the regions of high long-termseismic coupling overlap very well with the recent (>1940) largehistorical earthquake rupture areas (Fig. 3). The interface coupling,as inferred from the GPS time series over a period of 15 years, isthus coherent with the main seismic events over the last 70 years,
and could thus be a stable feature over many earthquake cycles. Thisresult also suggests that the Guerrero region, classically proposed asa seismic gap25 where a large earthquake could be expected, is in factreleasing most of the accumulated elastic strain aseismically. If largesubduction earthquakes occur in the Guerrero gap, their recurrenceperiodwould be about 3 to 4 times longer than in the areas boundingthe gap.
The 2014 SSE is the largest SSE recorded in Guerrero (equivalentto a Mw7.6 earthquake), and among the largest worldwide. Theextension of this SSE in the vicinity of the seismogenic zone, in thenorthwest limit of the Guerrero seismic gap, has led to the triggeringof aMw7.3 earthquake. The earthquake hypocentre is indeed locatedon the edge of the slow slip region, and the earthquake started onan adjacent seismic asperity, which already ruptured in the past(Fig. 4). Twomechanisms could explain this triggering. First, a staticstress perturbation induced by the SSE in the earthquake rupturearea, which induces its nucleation. This mechanism is supportedby the observation that the hypocentral area was loaded by about40 kPa of increased Coulomb stress during the first 80 days of
Figure 3 | Coupling of the subduction interface over di�erent timescales. a, Inter-SSE and long-term velocities are extracted from the GPS time series. Theexample of the north displacement for the CAYA time series is shown. b, Coupling during the inter-SSE periods, computed by inversion of inter-SSEvelocities. c, Long-term coupling, computed by inversion of long-term velocities. For b and c, the blue lines are large earthquake contours since 1940; thegreen line is the 2014 SSE slip contour (50% of maximum slip, that is, 0.15 m) of the 2014 SSE. Blue triangles, GPS stations used in each inversion study;white triangles, other stations not used.
0100−100
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Figure 4 | Schematic view of the frictional properties of the Mexicansubduction zone in the Guerrero region. The red contours show recent(after 1940) large earthquake rupture areas, including the 2014 Papanoaearthquake (thicker contour), and the black star indicates its hypocentrelocation. The blue contours show the SSE location: this area is highlycoupled on the short term and most of the slip deficit is released by SSEs.The surrounding purple regions have a low coupling ratio. The thickcontinuous blue line is the 2014 SSE (50% of maximum slip, that is, 0.15 mslip contour); previous SSEs are shown with broken lines (0.1 m slipcontour): dashed, dotted and dotted-dashed lines for the 2001–2002,2006 and 2009–2010 SSEs, respectively.
the SSE propagation, and very likely by previous SSEs (Fig. 4).This amount of stress is consistent with the static Coulomb stressincrease typically reported in earthquake triggering studies26. Note
that the equivalent of a Mw7.1 moment had already been releasedaseismically by the slow slip just before the Papanoa earthquake.The second possible mechanism would be an enhanced weakeningof the earthquake nucleation area, due to the SSE partial extent intothe ‘seismogenic’ locked region, that ultimately initiates a dynamicrupture. This is supported by rate-and-state numerical studies withdilatancy and/or thermal pressurization27,28 that show that severalSSEs may occur and die out until one transitions into a dynamicevent29. Both mechanisms can be postulated in the present case,since geodetic observations cannot rule out the local overlapping ofslow slip and earthquake rupture zone. Although postulated sincethe discovery of this class of phenomena in the late 1990s1, wepresent here the first clear evidence proving that a SSE can indeedtrigger a large subduction thrust earthquake.
The Guerrero region is exceptional as it reveals how spatialvariability in the frictional properties can lead to complexinteractions between SSEs and earthquakes (Fig. 4). In the so-calledGuerrero gap, large and recurrent SSEs release aseismically mostof the strain accumulated between the SSE episodes; the remaininglong-term coupling is thus low. However, in areas adjacent to theGuerrero gap, with high coupling on the long term, SSEs canpromote large earthquake nucleation.
MethodsMethods, including statements of data availability and anyassociated accession codes and references, are available in theonline version of this paper.
Received 22 March 2016; accepted 26 August 2016;published online 3 October 2016
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AcknowledgementsWe thank M. Bouchon for useful comments and discussions. We thank people whomaintain the cGPS networks in Guerrero (IG-UNAM) and Oaxaca (TLALOCNetprogramme) states, in particular J. A. Santiago at the Servicio MareograficoNacional-UNAM, S. I. Franco at the Servicio Sismologico Nacional-UNAM andL. Salazar-Tlaczani at UNAM-IGEF. Data from INEGI were also used for the Analysis.This work has been supported by a grant from Labex OSUG@2020 (Investissementsd’avenir ANR10 LABX56), and by the French spatial agency CNES (project TOSCASSEMEX). Portions of the GPS network were supported by the National ScienceFoundation under award EAR-1338091, by UNAM-PAPIIT projects IN104213-2,IN109315-3 and IN110514, and by Conacyt project 178058.
Author contributionsM.R. carried out data analysis, modelling and wrote the paper. H.P. edited the paper andperformed modelling. N.C. carried out GPS data processing and edited the paper. A.G.performed modelling and edited the paper. B.V. carried out modelling and edited thepaper. V.K. provided GPS data and edited the paper. T.L. performed modelling. A.W.edited the paper. E.C.C. managed the GPS networks. M.C. edited the paper.
Additional informationSupplementary information is available in the online version of the paper. Reprints andpermissions information is available online at www.nature.com/reprints.Correspondence and requests for materials should be addressed to M.R.
Competing financial interestsThe authors declare no competing financial interests.
MethodsGPS processing and inversion procedure. This study relies on the continuous GPSdisplacements recorded by the permanent GPS networks maintained by the UNAM(Universidad National Autónoma de México), the Insituto de Geofisica (IGF), theServicio Sismológico Nacional (SSN) and the TLALOCNet networks. Stations fromthe INEGI were also used. The raw data were processed to derive daily positiontime series with the GAMIT–GLOBK software31, and are referenced to the fixedNorth American Plate.
The GPS time series are decomposed in principal components using theprincipal component analysis inversion method (PCAIM19,20). Each PCAcomponent is then inverted for slip on the subduction interface, and thespatio-temporal evolution of the slip is obtained by linear combination of the threeprincipal components. This approach allows for fast and robust analysis of GPStime series without externally imposed temporal slip evolution. The subductioninterface is modelled as a three-dimensional surface with triangular dislocations,embedded in a Poisson-solid elastic half-space32. Inversion results are independentof the shear modulus G, but we assign the value 30GPa to G when estimating theequivalent seismic moments.
The inversion is performed using the linear least-square formalism described inref. 21. The model solution is spatially smoothed using a correlation functionbetween adjacent fault patches in the model covariance matrix (exponentialdecrease with a fixed correlation length λ=80 km)21. The optimal regularization isthen selected as a compromise between the data fitting (chi-square χ 2) and the sizeof the regularized solution (L2, squared norm of the model slip), through L-curvesas described in ref. 33, by varying the damping parameter σm0. σm0 controls therelative weight of the left (data fit) and right (model proximity to the initial null slipmodel) in the cost function that is minimized (see equation (1) in ref. 21). L-curvesare shown in Supplementary Fig. 1, left. The chi-square is given byχ 2= (1/ndata)
∑ndatai=1 ((dobs(i)−dmodel(i))2/σd (i)2), where ndata is the number of data
(daily GPS time series), dobs and dmodel are respectively the data and model vectors,and σd is the standard deviation (processing errors for GPS time series or standarddeviations as detailed below for inter-SSE and long-term trends). The size of theregularized solution is evaluated through the L2 norm on the cumulative slipdistribution: L2=
∑npatchesi=1 slip(i)2, where npatches is the number of discretized fault
elements and slip(i) is the cumulative slip amplitude for a given fault patch. Theslip direction is fixed during the inversion, and selected as the slip-directionproducing the lowest misfit (see Supplementary Fig. 1, right). Details of the selectedparameters are given in Supplementary Table 1 (Supplementary Methods). Theresult is validated by means of the restitution index34, which indicates that the localmean value of the slip is correctly inferred in the area of interest (seeSupplementary Fig. 8).
The inversion of the static values (co-seismic offset, inter-SSE velocities andlong-term velocities) is performed with the same method as the inversion ofprincipal components for the GPS time series. Slip deficit rates are converted intocoupling ratio using the Pacific VELocities (PVEL) plate model30, and convergencevectors vary laterally from west to east (from 5.8 to 6.4 cm yr−1).
Evaluation of inter-SSE and long-term velocities. Following the proceduredetailed in ref. 3, the secular velocities between the consecutive SSE periods arecalculated. Compared with ref. 3, one additional inter-SSE period (between 2010and 2014) is added and the linear trends can be estimated for a larger number ofGPS stations. The estimated velocities are thus more reliable, and they are similarbetween consecutive inter-SSE periods with a 95% confidence level. For eachstation and each displacement component, the mean inter-SSE velocity iscomputed as the average of the velocities of all available time periods, weighted bythe number of data available for each period. The evaluated velocities are then usedto correct the time series for the inter-SSE trend, and estimate the inter-SSEcoupling on the plate interface.
The long-term linear trends represent the resulting trends over a completenumber of SSE strain loading–release cycles. A SSE cycle is defined as the time spanbetween two sequential extrema of the GPS time series previously smoothed by a10-day-moving-average filter. Each extremum is detected as the transition betweenthe transient SSE motion and the onset of the inter-SSE loading phase. Only GPS
stations that recorded at least one complete SSE cycle are kept in the analysis. TheSSE cycles containing co-seismic offsets are also excluded from this analysis. Thelong-term linear trends are finally evaluated for each time series component as thetrend between the first and last evaluated extrema. Standard deviations of thelong-term linear trends are assigned depending on the duration of the evaluationperiod (time between the two extrema considered). The standard deviationdecreases when longer time periods are considered. The estimated short- andlong-term velocities and associated error bars are shown in Supplementary Fig. 3.Note that the long-term coupling map (Fig. 3c) is obtained directly by invertinglong-term trends, whereas in ref. 3 it was estimated indirectly by summingshort-term slip deficit and SSE contribution.
Correction for co-seismic offsets. Co-seismic offsets for the 18 April and 8 Mayearthquakes are removed from the GPS time series using the following procedure.When the cGPS time series are continuous during the mainshock, the co-seismicoffset is computed as the difference between the averaged GPS daily position twodays before the earthquake and two days after the earthquake, for each GPS stationand each displacement component. Co-seismic offsets are considered significantwhen they are larger than twice the GPS standard deviation in which case they areremoved from the GPS time series. As four GPS stations have missing data aroundthe date of the Papanoa earthquake, we estimate their co-seismic offset consideringa different strategy. We compute the SSE slip model using all of the other GPS timeseries (corrected from the offsets) and use this slip model to predict the GPS timeseries of the stations with missing data. The co-seismic offset is then computed sothat the modelled time series match the data in the first days immediately followingthe data gap (offset computation averaged over two days). This strategy ensuresthat the co-seismic offsets are coherent between adjacent stations. This strategy is aposteriori validated by noting that the slip model of the Papanoa earthquake basedon our estimated co-seismic offsets is consistent, both in magnitude and slipdistribution, with a slip model inferred by seismological data for this earthquake18.
Coulomb stress change computation. The Coulomb failure stress change1CFS isevaluated on the subduction plane, from the pre-earthquake slow slip distribution.We considered areas with significant slip (>50% of maximum slip) therebyconcentrating the slip distribution. The Coulomb failure stress change is computedby1CFS=1τ−µ1σ , where the friction coefficient µ=0.6, and1τ and1σ arerespectively the shear and normal stress variation computed using the formulationin ref. 35. The normal stress variations are low (<0.1×105 Pa), and thus the choiceof the friction coefficient has a limited influence on the estimated1CFS values.Tests on the influence of cutoff slip values and inversion smoothing on the obtained1CFS are displayed in Supplementary Figs 10 and 11, and show that the loading ofthe Papanoa epicentre by the 2014 SSE is a robust feature of our inversions,providing that the degree of smoothing of the SSE slip model is not excessive.
Code availability. The PCAIM code used for the analysis of GPS time series can bedownloaded from http://www.tectonics.caltech.edu/resources/pcaim.
Data availability. The GPS time series used for the analysis are available from thecorresponding author on request.
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