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Project number: 282862

Project name: Strategies and tools for Real Time Earthquake Risk Reduction

Project acronym: REAKT

Theme: ENV.2011.1.3.1-1

Towards real-time earthquake risk reduction

Start date: 01.09.2011

End date: 31.08.2014 (36 months)

Deliverable: 4.11 Design of a mobile seismic network for fore-shock/aftershock early warning

Version: 1.0

Responsible partner: GFZ

Month due: 34 Month delivered: 36

Primary author: Stefano Parolai (GFZ) 31.07.2014

Date

Reviewer: Aldo Zollo (AMRA) 24.09.2014

Date

Authorised: Paolo Gasparini (AMRA) 01.10.2014

Date

Other contributors: Dino Bindi (GFZ) , Tobias Boxberger (GFZ)

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Dissemination Level

PU Public X

PP Restricted to other programme participants (including the Commission Ser-vices)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)

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Acknowledgement

The research leading to these results has received funding from the European

Community’s Seventh Framework Programme [FP7/2007-2013] under grant

agreement n° 282862.

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Content

List of Figures 5

1 Introduction 6

2 Multi parameter system acquisition 8

2.1. PART I. Multi parameter system acquisition:

hardware development

8

2.1.1 State of knowledge and preliminary work: 9

2.1.2 Multi Parameter Prototype 11

2.2 PART II. Multi parameter system acquisition:

software development

14

2.2.1 Criteria for event detection 14

2.2.2 Damage forecasting 16

Conclusion 24

References 25

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List of Figures

Fig. 1 Schematic showing the general concept of the multi-type/scale/parameter

system that is under development during the REAKT project for the exam-

ple of interactions between earth-quakes and flood defence dykes

8

Fig. 2 Test of a low cost single frequency GNSS unit in comparison to strong

motion sensors and optical camera. Aim is to derive reliable information

about displacement, velocity and acceleration in the seismic frequency

band

9

Fig. 3 Results from the combined processing of the GPS, accelerometer, and

camera image data for a sliding box experiment (Tu et al., 2013). a) Re-

sidual time series between the accelerometer and GPS-based displace-

ment time series. b) Displacement time series U retrieved by subtracting

the smoothed Udif from the accelerometer-based displacement, validated

by the dual-frequency GPS (blue) and camera results (red). c) Velocity

time series, compared with the camera observations

10

Fig. 4 First tests of the new network nodes with attached sensors under office

conditions. Different communication ways (LAN, WLAN, UMTS), energy

consumption, and timing possibilities are tested here.

13

Fig. 5 Top: The recording of the M 5.9 20th May 2012 Emilia earthquake at the

station MDN located at nearly 38 km (gray line) and its filtered version

(black). The vertical line continuous indicates the time of the identification

of the event, while the vertical dashed line shows the identification of the

event on the original strong motion recording. Bottom: the STA/LTA curves

obtained by analyzing the unfiltered (black) and filtered (gray) signals.

15

Fig. 6 Top: A sample of the signal analysed for testing the procedure including

the M 4.2 11th October 2013 earthquake. Bottom: the corresponding STA-

LTA function.

17

Fig. 7 The predicted PGV values (84% and 16% confidence intervals in gray

and the mean in black) and the EW (gray) and NS (black) velocity record-

ings of the M 5.9 20th May 2012 Emilia earthquake at the station MDN.

The horizontal black lines indicate the threshold values used in the sema-

phore alarm procedure set to 3.4 cm/sec and 8.1 cm/sec, respectively.

18

Fig. 8 The three matrices adopted for the semaphore system suggest as support

for taking actions.

19

Fig. 9 Top: The predicted mean-, mean, and mean+ PGV values (gray ,

black, and gray dots respectively) obtained by analysing 11 recordings of

the M 5.9 20th May 2012 Emilia and the M 6.3 6 April 2009 L’Aquila earth-

quakes. The black squared indicated the observed values. Bottom: the

same as top panel but for the recordings contaminated with SOSEWIN

noise.

20

Fig. 10 Top: The NS recording of the M 5.1 9th April 2009 Aftershock of the

L’Aquila earthquake at the SOSEWIN station installed outside the City Hall

of Navelli (Picozzi et al.,2011). Bottom. The observed (gray) and simulat-

ed (black) recording at the top floor of the building.

22

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Design of a mobile seismic network for foreshock/aftershock early warning

1 Introduction

After a disastrous earthquake the possibility of reducing losses due to the damage created by the following aftershocks might be enhanced if early warning/rapid response systems can be deployed rapidly in the field. In this case, in fact, the deployed instruments might serve for:

1) Forecasting the expected damage to a structure during the aftershocks sequence 2) Implementing an On Site Early Warning for infrastructure (OSEW) 3) Assessing the expected damage to nearby structures soon after an aftershock

occurrence 4) Validating the damage forecast based on probabilistic approach and updating the

fragility curves depending on the recorded ground motion 5) Monitoring the structural behaviour

In the context of structural health monitoring, damage is defined as changes introduced into a system that adversely affect its current or future performance. In this deliverable, the damage analysis is interpreted as the assessment of the probability that the structure reaches a given damage level conditional to the value assumed by the demand parameter. Typical examples are the fragility curves expressed in terms of a particular strong motion parameter (e.g PGA, PGV, SA), or through considering damage matrixes in terms of macroseismic intensity. Therefore, in the following we refer to observed damage when the damage state is determined by real-time observation collected by a multi-parameter system during the shaking, while we refer to forecasted damage when the demand parameter is forecasted from parameters recorded before the arrival of the strong motion phase (e.g., using P-to-S waves empirical relationships developed in the context of on-site early warning). The ideal system that can fulfill these tasks, requires a large flexibility both in data transmission and communication, easiness of installation (free-field or directly within the target infrastructure), stand-alone capability of operating as well as possibility of generating arrays, multi-parameters detection. While these characteristics are of main importance any kind of early warning system (regional and on site), here the attention is focused only on the onsite early warning. In this context, the possibility for a single instrument to detect a possible dangerous event in a reliable way and to forecast the expected risk for a target structure or for several of them is a challenging pre-requisite. This is particularly true considering the highly noisy environment where this kind of installations are generally carried out. Until recent, most of the Onsite Early Warning Systems attempt a rapid estimation of the incoming danger either through a rapid (and first order) estimation of the magnitude and location of the happened event, and, then estimating the possible ground motion that the site will experience (Nakamura 1984; Nakamura 1988) or by directly estimating the

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ground motion depending on the peak values of the ground displacement measured during the first seconds of first arriving P-waves (Zollo et al., 2010). In the last decade, in order to increase the efficiency of the early warning systems, an end-to-end approach where the concept of early warning was coupled with the expected structural performance (e.g. Iervolino, 2011). Following this approach, early warning, structural analysis, damage and loss analyses are combined in an unique performance-based framework on which decision making procedure can be established (e.g. Cheng et al., 2014). Exploiting the computational power of modern sensing units for building monitoring, the implementation of this concept can be transferred to each unit, implementing in a such way a decentralized performance based early warning. In this deliverable, starting from the experience gained before and during the REAKT project and after having analysed and taken into account the requirements described above, indications on how a mobile early warning/rapid response network for foreshocks/aftershocks activity should be designed are provided. Examples of possible performance of this system are discussed.

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2 Multi parameter system acquisition

In the following, we describe the design of the early warning system for aftershocks early warning, in the contest of an early warning system (at least for what regards the hardware development) that is designed for multi-hazard purposes. The document is divided in Part I and Part II: in the former, we provide an overview of the hardware development while, in the latter, we describe software developed for the data processing.

2.1 PART I. Multi parameter system acquisition: hardware development

A new generation of a mobile multi-parameter Early Warning System is being designed, first nodes are being assembled and tests started with a broad variety of sensors. Single stations are forming a multi-parameter network for ground based multi-parameter real-time monitoring, and optimized for early warning applications. The design strategy ensures a highly flexible and modular system for ground based observation, which can be directly integrated into regional based monitoring systems. Information derived from the sensor network gives ground truth and geo-referenced information to be used for on-site early warning and decision making, for example in on site tsunami early warning or landslide monitoring systems. The new generation of multi-parameter observation makes it possible that sensor nodes send not only discrete sensor information about local conditions but also complex streams of information such as photos for change detection and early warning.

Figure 1: Schematic showing the general concept of the multi-type/scale/parameter system that is under development during the REAKT project for the example of interactions between earthquakes and flood defence dykes.

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2.1.1 State of knowledge and preliminary work:

Sensor networks play an increasing role in the geosciences especially in monitoring under field conditions, for state of health monitoring and in seismic early warning applications (Fleming et al., 2009, Picozzi et al., 2010a, Picozzi et al., 2010b, Picozzi et al. 2014, Wenzel et al., 2014). Here, self-organizing low cost sensor networks represent a new class of systems in geo-monitoring. The performance of these sensor networks in terms of data throughput and long-term availability is an active area of research (Fischer et al., 2012). For example, the Humboldt Wireless Lab 1 (HWL) is a wireless self-organizing indoor and outdoor mesh network where applications are evaluated. For medium-sized networks in seismic monitoring applications the experience is very positive (Picozzi et al., 2010b, Pilz et al., 2013a) even with higher data rates. Dense sensor networks help to overcome uncertainties in widely used standard types of analysis methods for spatially-variable parameter assessment. In contrast to highly precise single-station systems, low-cost sensor networks can be installed more densely. Therefore, fewer assumptions and/or interpolations are necessary for assessing for example strong ground shaking and earthquake intensities. Moreover, with the use of new multi-parameter sensor networks a detailed local picture about the critical state of infrastructure or landslides can, in principle, be monitored and reported upon in real time.

Figure 2: Test of a low cost single frequency GNSS unit in comparison to strong motion sensors and optical camera. Aim is to derive reliable information about displacement, velocity and acceleration in the seismic frequency band.

Real-time detection and precise estimation of strong ground motion are crucial for the rapid assessment and early warning of geohazards such as earthquakes, tsunamis, landslides and volcanic activity. This challenging task can be accomplished by combining multi-parameter information (Tu et al., 2013, Pilz et al., 2013b, Walter et al., 2013a). A promising combination is GPS and accelerometer measurements extended by camera image information (Parolai et al., 2013) because of their complementary capabilities to resolve broadband ground motion signals. However, for implementing an operational monitoring network of such joint measurement systems, the newly designed

1 http://hwl.hu-berlin.de/

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cost-effective system under development will be rigorously tested. Preliminary results of a new approach for the joint processing of single-frequency GPS and MEMS-type accelerometer data in real time are promising (Tu et al. 2013, Tu et al., 2014, Parolai et al., 2013), as they demonstrate the adequate performance of the combined analysis of multi-parameter information (see Figure 3).

Figure 3: Results from the combined processing of the GPS, accelerometer, and camera image data for a sliding box experiment (Tu et al., 2013). a) Residual time series between the accelerometer and GPS-based displacement time series. b) Displacement time series U retrieved by subtracting the smoothed Udif from the accelerometer-based displacement, validated by the dual-frequency GPS (blue) and camera results (red). c) Velocity time series, compared with the camera observations.

Detecting and monitoring slow ground motion and slope stability is one of the main challenges in landslide monitoring. Although modern monitoring networks are in place at many landslide areas, no operational mobile monitoring system currently exists. Quantifying the landslide area and the occurrence of sliding episodes is highly valuable, not only for enabling the provision of early warnings but also for facilitating an understanding of the physics of landslides. Based on recent findings of ambient seismic

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noise observation on a landslide (Pilz et al., 2013a; Pilz et al., 2013b), it has shown that with a limited number of stations and recording times of several hours, basic key parameters like the fundamental resonance properties of a landslide body, as well as detailed images of the local S-wave velocity structure can be obtained and possibly monitored, even under pronounced topographic conditions. There is clear evidence that ground motion on potentially unstable slopes can be amplified considerably with a pronounced directional character and with maxima oriented along potential sliding directions. In addition, the application of digital image correlation (Walter et al., 2013a, Walter et al., 2013b) is becoming an increasing important tool for ground-based remote sensing techniques, such as for volcano and landslide monitoring. This indicates the possibility of tracking changes, possibly triggered by ground motion vibration (earthquakes or train passages). Images taken before and after the sliding episodes reveal the location of sliding events and cause lateral pixel offsets. Furthermore, preliminary tests (Parolai et al., 2013) have shown the possibility of image acquisition directly with low cost sensors and their usage for high-precision relative displacement assessment. These recent findings indicate the importance for using multi-parameter information in a combined analysis procedure. 2.1.2 Multi Parameter Prototype Ground-based information provides essential reference data for assessing the accuracy of other information, such as Earth observation products. In this deliverable, a new multi-parameter real-time sensor network for early warning has been designed and different sensor combinations are under test, with the aim of it having the potential for different applications worldwide. Different sensors and sensor types are identified which will allow the simultaneous monitoring and analysis of ground shaking (broadband seismometers, geophones or acceleration), deformation and displacement (GPS), optical sensors (camera picture streaming) and meteo- and hydro-monitoring (weather stations, tide gauge, pore pressure). Different means of analysis are under development and implementation, depending on the required lead time, either directly using the calculation capability of each single node that make up the self-organising network, or in the early warning centre. The sensors may be used in both permanent installations in high-risk areas, or as a temporary network during a task force mission following an event. While the technology used in the SOSEWIN-nodes enabled a more or less pure acquisition unit, the new hardware has more computing performance (~1.7 GHz quad core processor, 2 GB RAM) and is capable for more sophisticated data processing and analyses steps directly on the nodes in early warning systems. In addition, the embedded hardware platform Odroid U/X has been chosen for their stability, extensibility, compatibility and speed. Stability usually means stability of the hardware itself, as well as in terms of provided 3rd party proprietary firmware. Extensibility refers to the ability to attach sensor hardware to existing ports or to extending the platform hardware. The operating system is Linux based. Since the initially used SOSEWIN platform built upon on OpenWrt showed some weaknesses when dealing with an extension to modern system requirements, a switch to another operating system was important. While the SOSEWINs form a more or less pure acquisition unit, the newly designed system is more powerful in terms of computing performance (1.7 GHz quad core processor, 2 GB RAM) and capable for more sophisticated data processing and analysis steps directly on the nodes.

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Within REAKT, a new prototype of sensing node with new processor and additional sensor connections has been designed and first nodes are developed. With respect to the older version, the prototype of the sensing node is equipped with the Common Ac-quisition Protocol Server (CAPS) acquisition servers developed by GEMPA Company (http://www.gempa.de/caps/). One of the advantages of CAPS over SeedLink is the possibility of transfer multi-sensor data from the station to the data center using a unified protocol (e.g. seismic data and images), which is not possible with seedlink. This fea-ture allows the optimization of the joint transfer of seismic data and images in a unique data-stream. The strategy for the new multi parameter prototype is to fulfill the needs for different types of application such as for regional and on-site earthquake early warning and rapid response systems, structural health monitoring and site-effect estimation by two-dimensional arrays. The system is designed in a modular way to allow different configurations with respect to attached sensors and communication interfaces tailored to the different application. Referring to the possible sensors, the prototype is capable to be instrumented with standard strong motion, weak motion sensor, broadband sensors, MEMS sensor including accelerometer and gyroscope, camera, temperature and humidity sensor and a low cost GNSS system. The data transmission follow the real time approach and dependent on either single station or network topology, the communication can be managed via LAN, UMTS or a self organizing wireless mesh network. The hardware base frame consist in a powerful quad-core arm processor and the high resolution CUBE digitizer board. The hardware performance allows processing the data on the node level, such as using on-site early warning software that performs the standard operations (event detection, prediction of some strong motion parameters of interest for S-wave using information from the first few seconds of a P-wave arrival, real-time update). Moreover the hardware can be completed by components to issue an audio and visual alarm. Data acquisition is managed by the CAPS protocol, developed by GEMPA due to the requirements of a unified protocol for e.g. seismic data and images. All micro computer and digitizer boards are installed in waterproof outdoor metal cases and the connectors fulfil the IP67 standard.

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Figure 4: First tests of the new network nodes with attached sensors under office conditions. Different communication ways (LAN, WLAN, UMTS), energy consumption, and timing possibilities are tested here.

Application layer: regional and on-site earthquake early warning and rapid response systems, structural health monitoring and site-effect estimation by two-dimensional arrays Sensor layer: standard strong motion and weak motion sensor, high quality broadnband seismic sensors, MEMS sensor including accelerometer and gyroscope, camera, temperature and humidity sensor, low cost GNSS system Communication layer: LAN, UMTS, self organizing wireless mesh network Hardware layer: powerful quad-core arm processor and 2GB RAM, high resolution 24 bit CUBE (http://www.omnirecs.de/) digitizer boards (see table), touch display, water-proof outdoor metal cases. Broadband and good weak motion sensors can be connect-ed to a sensor node as the Cube digitiser is being the main board for AD-conversion, better dynamic range and better anti alias filter than the SOSEWIN digitiser

AD Converter Performance @ 100sps

Gain 1 Gain 16 Gain 64

HiRes SNR 125,6 dB 122,2 dB 113,5 dB

eff. Bits 22,4 Bit 21,8 Bit 20,3 Bit

LowPwr SNR 122,6 dB 120,7 dB 113,0 dB

eff. Bits 21,9 Bit 21,5 Bit 20,2 Bit

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GPS/GNSS Is being tested offline and in file-based simulation of real-time performance. Under de-velopment is real-time connection of an UBLOX GNSS unit Communication Is being tested and reported about in Deliverable 4.3 Status of the test phase: 5 different units are assembled and final tests will be ready soon. Different commercial micro computers are tested with the CUBE digitizer board, with different combination of communication link (LAN, UMTS, WLAN), number of channels and sensors (MEMS sensor, camera, low cost GNSS microphone, etc). 2.2 PART II. Multi parameter system acquisition: software development

In the second part of this Deliverable, we describe the software for the data processing to be installed in the sensing units.

2.2.1 Criteria for event detection

Considering the short lead time and the necessity of predicting the incoming S-wave motion in the very first seconds after the event identification, the detection of the first P-wave arrival is a crucial point for Onsite Early Warning Systems and therefore also for a portable early warning rapid response system. While traditional methods for event detection (based on the visual inspection of the seismogram) cannot obviously be used, also the most recently proposed, in particular those dealing with time-frequency analysis based algorithm (e.g. Galiana-Merino et al., 2008 and reference therein) might be not efficient enough or not appropriate for a real time data analysis. Standard short-term over long-term average (STA/LTA) algorithms working in the time domain has to be preferred although they might be more sensitive to false event detection especially in noisy urban environment (Küperkoch, 2012) .

In order to reduce this problem and considering that the system under development should be reliable when dealing with at least moderate size earthquake, the event detection could be carried on a low pass filtered version of the original signal. Low pass filtering can be recursively implemented in the data acquisition and might be carried out either using standard low pass filters or Gaussian ones.

The corner frequencies of the filter should be chosen depending on the local noise conditions but, considering that human activity is mainly generating seismic noise at frequencies larger than 1-2 Hz and the corner frequency of damaging events is occurring at frequencies lower than 1 Hz, a low pass filter at 1 Hz might be tentatively suggested (Bormann et al., 2013, Bormann and Wielandt, 2013)

Similarly, the width of the Gaussian window used to smooth the data can be chosen accordingly in order to filter out the spurious high frequency signals. Although this procedure cannot fully avoid the triggering of events on phases different from the P-waves (for example on the S-waves of relatively small earthquakes) it can reduce the false-positive event detection.

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As it will be clarified later, it is worth mentioning that a false event detection does not imply directly a false alarm, since the alarm procedure is based on the forecasted S-wave ground motion.

Figure 5 show exemplarily the application of this procedure to the recording of the M 5.9 20th May 2012 Emilia earthquake at the station MDN located at nearly 38 km from the hypocenter. The STA window was fixed to 0.5 seconds, while the LTA one to 6 seconds. These values, together with the threshold for the detection fixed to 4, were found to provide after trial and error test on several recordings, the best compromise between a reliable event detection and sensitiveness to spurious signals. The original record has been contaminated with signal noise recorded at one SOSEWIN station installed in the AHEPA hospital (see deliverable 7.7) in Thessaloniki (Greece) in order to simulate a possible acquisition data of a low cost system (SOSEWIN) in a noisy environment. Figure 5 show the noisy recordings (gray line) and the real-time Gaussian filtered signal (black line). The STA/LTA procedure applied to the filtered signal (Figure 5, bottom) shows a fair detection of the event (black vertical line in the top panel). The arrival time, in fact, is detected 0.34 seconds after the correct event arrival as estimated by the same algorithm when applied to the original (i.e. without noise addition) recording (black dashed line).

Figure 5. Top: The recording of the M 5.9 20th May 2012 Emilia earthquake at the station MDN located at

nearly 38 km (gray line) and its filtered version (black). The vertical line continuous indicates the time of

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the identification of the event, while the vertical dashed line shows the identification of the event on the original strong motion recording. Bottom: the STA/LTA curves obtained by analyzing the unfiltered (black) and filtered (gray) signals.

It is worth noting that the application of the STA/LTA procedure to the filtered strong motion recording (Figure 5, bottom, gray line) in general increases the amplitude of the STA/LTA function. Therefore, the earlier event detection over filtered signal with respect to the application to the unfiltered one, slightly increases the already short lead time.

It is worth mentioning that for installation nearby industrial areas, likely affected by seismic noise affecting a particular frequency, the signal should be easily filtered including in the system the possibility of using a recursive notch filter.

The implementation, of course, also in this case is relatively easy and does not affect the real time analysis of the data.

The event detection based on the above mentioned criteria was tested over one week of SOSEWIN recordings collected by two stations inside the AHEPA Hospital (one installed at the top of the building and one in the first floor) in the framework of REAKT. Unfortunately no recordings with the SOSEWIN system were available in free-field. On 11th October 2013, a local M 4.2 earthquake, occurred at 38 km from the site, was recorded by the system (Figure 6). During the whole period the event detection threshold was overstepped only for the small local event indicating that with appropriate fine-tuning of the STA/LTA filters and parameters a quite robust event detection might be achieved. Although these results are not definitive they hint to the possibility of dramatically reducing the incorrect event detections when even only one station is used.

When the system is composed by more than one station installed nearby, the event detection in case of local noise transients can be improved by cross checking the information between nearby sensors in real time via the wi-fi communication.

2.2.2 Damage forecasting

One of the main tasks that a portable early warning rapid response system should be able to fulfill, is the capability to provide an estimation of the damage that can affect a structure either before that the strong motion phase is arriving or soon after the ground shaking elapsed.

In the first case, appropriate actions can be taken following the suggestion of rapid cost/benefit analysis, in the second case, the estimated damages might be used for updating the fragility curves of the structure(s), leading to a time-dependent loss analysis.

Important, is that these estimation are carried out on the single system although this does not preclude that final decision can be taken, depending on the cases, considering the estimations coming from different sensors.

Of course, although these estimations are obtained by using the recordings of a single instruments, in the case that array of instruments are available, the decision on the necessity to issue an alarm can be taken also considering the information coming from other sensors.

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In the case of damage forecast, the experience gained by cooperating with different partners in the project suggests that a fair estimation of damages that the structure might suffer could be obtained in real time as follow:

1) Analysing the ground displacement (PGD) in the first few seconds in the P-wave arrival of the recorded vertical strong motion;

2) Estimating through empirical relationships the expected peak ground velocity (PGV) on the horizontal components;

3) Estimating the correspondent damage level (either using Intensity based relations or fragility curve based approaches).

Figure 6. Top: A sample of the signal analysed for testing the procedure including the M 4.2 11th October

2013 earthquake. Bottom: the corresponding STA-LTA function.

An On Site Early Warning software, developed in collaboration with GEMPA GmbH (http://www.gempa.de) was installed in several SOSEWIN nodes and it is currently in the testing phase (see deliverable 4.3). The software allows a customized damage assessment of the structure looking at the probability of occurrence of different damage states. This means that the fragility curves are uploaded directly in each node and used

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for the decentralized real-time analysis. The results are transmitted to a remote user but they might be directly used to activate alarm systems (sirens etc).

It is worth noting that the declaration of alarm it is not based on the event detection but on the expected state of damage for the structure. That is, a false event identification (when for event an earthquake is meant) might not necessarily lead to a false alarm.

Figure 7 shows an example of the application of the above described procedure to the recording depicted in Figure 5. The expected PGV is estimated continuously starting from the ground displacement derived from the vertical component of motion. Differently from standard procedure, both the mean value and the 16% and 84% confidence interval values PGVs are estimated. In the case at hand the relationships proposed by Zollo et a. (2010) is adopted. The obtained values (for all the three cases) are continuously compared with the threshold values set in the system and different alarm levels can be continuously activated. That is, although the analysis is carried out in the first 3 seconds after the event detection, a red alarm, for example, can be issued well before the end of the procedure.

Figure 7. The predicted PGV values (84% and 16% confidence intervals in gray and the mean in black) and the EW (gray) and NS (black) velocity recordings of the M 5.9 20

th May 2012 Emilia earthquake at

the station MDN. The horizontal black lines indicate the threshold values used in the semaphore alarm procedure set to 3.4 cm/sec and 8.1 cm/sec, respectively.

Figure 7 also shows the comparison of the estimated values with the NS and EW ground velocity recordings of the same event at the same station. The observed PGV is lying within the range of forecasted values and it oversteps the first threshold level adopted here set to 3.4 cm/s, roughly corresponding to a macroseismic intensity V.

Generally, the PGV information is used as input for semaphore systems (Zollo et 2010), where it (generally only the mean value) is elaborated and used to help decision makers in taking a rapid and robust actions.

Here, considering the availability of the three estimation of the PGV, a modified semaphore system is proposed. First, two ground velocity threshold are defined, with the first one set to a value where light damages might occur (in this case a 3.4 cm/s value was used) and the second one to values where light damage are potentially happening (8.1 cm/s, in the case at hand).

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Figure 8. The three matrices adopted for the semaphore system suggest as support for taking actions.

These thresholds might of course be set in different ways depending on the end-user requirements or the task to be fulfilled after the deployment of the portable system. However, the general concept of the proposed semaphore scheme does not have to be

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modified. The semaphore system considers three matrices (Figure 8) each one drawn depending on the value assumed by the estimated PGV when considering the mean plus the standard deviation value. The first matrix is therefore drawn for the analysis

when the mean + value is >than 8.1 cm/s, the second one when its value lies between

3.4 cm/sec and 8.1 cm/sec and the third one when the mean +value is smaller than 3.4 cm/sec. The three row and columns of the matrices are relevant to the same interval

of values but for the mean and mean -estimates, respectively. These matrices, in principle allow a more conservative alarm detection (i.e. include the possibilities of larger shaking and therefore tend to lower the alarm level) than a simpler system based on the mean estimate alone and their design is taking into account the largest degree of uncertainty for decision taken when only the information from one instrument is available. However, considering the capability of the suggested system to communicate data in real time also amongst sensors, a cross-analysis of the alarm status between different sensors in possible when small arrays are deployed and the actions can be taken based on a weighted joint analysis of them.

Figure 9. Top: The predicted mean-, mean, and mean+ PGV values (gray , black, and gray dots

respectively) obtained by analysing 11 recordings of the M 5.9 20th May 2012 Emilia and the M 6.3 6 April

2009 L’Aquila earthquakes. The black squared indicated the observed values. Bottom: the same as top panel but for the recordings contaminated with SOSEWIN noise.

.

Figure 9 shows the estimated PGV values versus the observed ones for different recordings of the M 5.9 20th May 2012 Emilia and the M 6.3 6 April 2009 L’Aquila earthquake. The recordings have been chosen to mostly lie within the epicentral distance range of validity of the equation of Zollo et al., (2010). For sake of testing, we also considered a recording at much shorter epicentral distance (~13 km). Figure 9

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shows that the results obtained by using the original recordings (top) or those obtained after contamination with SOSEWIN noise, does not differs significantly in terms of forecasted PGVs. Anyway, a small tendency of the observed values to be slightly larger than the predicted ones can be observed. This effect might be related to the way the selection of the recordings was carried out, that is without paying attention to the soil classification of the stations. It therefore might be possible that the results shown in Figure 9 are biased by stations affected by site amplification of the ground motion.

The results depicted in Figure 9, when translated into possible decision process by means of using the matrices proposed above, lead to, in the majority of the cases (10 over 11 when the original recordings are analysed and 9 over 11 when the SOSEWIN simulated ones are considered), a correct suggestion for the possible alarm. When the original recordings are considered only in the case of the Avezzano station (AVZ) the proposed matrices would fail by suggesting a green-light status while the ground motion was observed to largely overstep the adopted threshold of 8.1 cm/sec. Anyway, this station is located inside a deep sedimentary basin well known for generating large increases of ground motion due to 2D-3D site effects (Cara et al., 2011). In such cases of installation of a portable system, safety correction factors could be adopted for the threshold system.

It is worth noting that although with an underestimated value of PGV, the proposed approach would have released a red alarm status at the station located at short epicentral distance (MRN) for the recording of the Emilia earthquake. The red alarm, adopting the procedure suggested above would have been issued after 1.2 seconds from the event detection. Due to the short distance between the epicenter and the recording station only nearly 0.3 seconds would have been left before the theoretical S-wave arrival but, more importantly, at least 2.5 second before that the ground motion on the horizontal component would have overstep the threshold value of 8.1 cm/sec. This indicates that on site Early Warning Systems might still provide useful Early Warning Alarms even at epicentral distances shorter than what is generally expected.

Similar considerations can be drawn when the recordings contaminated by SOSEWIN noise are analysed. However, at one station (ZPP) recording the Emilia earthquake a red alarm would have been released while the PGV was not observed to overstep the 8.1 cm/sec threshold (the PGV value recorded was 4.4 cm/sec). Considering that such a level of shaking is strongly felt by the population, this false alarm (based on the expected possible damage), would not affect the credibility of the system in the population. Anyway, it might create not necessary loss (economic one) when release for the safety of critical infrastructures and industrial buildings. This indicates that in such a specific cases a finer tuning of the threshold levels and of the matrices design should be attempted for.

Regarding the second case mentioned at the beginning of this paragraph, that is the possibility to use a portable Early Warning rapid response system to forecast the damage to buildings, we suggest:

1) That the system should carry out in real time the monitoring of the structure by means of multi-parameters data acquisition and analysis inside the structure ( see also Deliverable 4.3)

2) That, following an innovative idea proposed here, the recording in the free field of a single instrument can be used to predict in real-time the level of shaking (and in particular of displacement) within one or more nearby buildings.

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Figure 10. Top: The NS recording of the M 5.1 9th April 2009 Aftershock of the L’Aquila earthquake at the

SOSEWIN station installed outside the City Hall of Navelli (Picozzi et al.,2011). Bottom. The observed (gray) and simulated (black) recording at the top floor of the building.

While the simulation of the shaking of a building at different floors can be realized by continuum structural model such as shear beam model or Timoshenko beam model (e.g. Cheng et al., 2014), for simple structures where the fundamental translational mode of vibration is dominating, the shaking of the structure at the top floor can be simulated by implementing recursive calculation of the acceleration and/or of the displacement that a single-degree-of-freedom would experience at the same instant.

The fundamental resonance frequency of the structure can be obtained either by rapid seismic noise measurements or, when this is not possible, by simple height of the building versus resonance frequency relationships. The damping can be set, at the beginning of the temporary installation of the portable system, to standard values from literature. Both the resonance frequency and the damping value can be later updated from remote, considering the values calculated by analyzing the first small aftershocks.

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Due to the low computation demand of the procedure, the predicted shaking can be calculated for several structures characterized by different a-priori known structural models. In such a case, the horizontal ground motion recorded from the event detection, as declared by analysing the vertical component, can be used as seismic input for estimating in real-time the response of the different structural models representing different buildings located close to the station.

An immediate check of the values predicted for the relative displacement of the structure provides first-order information about the level of drift that the building suffered. If threshold values are exceeded, it is likely that the displacement estimated in the following time is not representative anymore of the behavior of the structure but nevertheless information about the overstepping of a certain first limit state is provided.

Furthermore, improvements for real time estimation of the expected shaking that can be experienced by a building or a series of them can be obtained by using more complicated and time dependent descriptions of the building dynamic behavior.

Figure 10 shows the results obtained while applying the described procedure to the recordings of the M 5.1 9th April 2009 aftershock of the L’Aquila earthquake at the SOSEWIN station installed outside the City Hall of Navelli (Picozzi et al., 2011). In this case the real time simulation is carried out on the horizontal ground motion component corresponding to the longitudinal direction of the structure for which Picozzi et al. (2011) estimated both the frequencies of vibration and the damping of the fundamental mode.

Accordingly to Picozzi et al. (2011) a fundamental frequency of 2.54 Hz and a damping of 10% were used. Figure 10 shows that the simulated accelerogram at the top of the structure well approximates the observed one, especially during the strong motion phase. Please, note that due to the small magnitude of the event, the small signal–to-noise ratio, and the not optimized setting of the event triggering, the event detection starts with the S-phase, therefore running the simulated seismogram calculation on the stronger motion phase.

Tests we carried out, using lower damping values but consistent with those estimated by Picozzi et al. (2011) for smaller magnitude aftershocks, showed that while a lower damping helps in better fitting the coda of the accelerogram, they are less appropriate for reproducing the strong motion phase. This results, hinting to a damping increase during the strong motion phase and, than to its fast recovery in the coda, although of major interest and consistent with the observation of Picozzi eta al (2011) for the behavior of the S-wave velocity propagation in the structure, is worth of deeper investigation before final conclusion can be drawn.

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Conclusions

In this report we outline the design of a portable seismic network for foreshock/aftershocks early warning. The design of the hardware is meant to allow also multi-hazard early –warning that might be of primary interest where also secondary but important effects like landslides should be considered. Although more tests must be carried out, the technical solutions proposed seems to respond to the required demand of flexibility, easiness of installation and quality of the acquired data. The software solutions suggested for the rapid event detection and analysis are conform with the required speed of calculation and robustness of the results for helping decision makers. While more tests should be carried out in different recording conditions and having in mind different targets (residential, industrial buildings etc) the results presented here look encouraging.

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