Tremors in the Mexican subduction zone

Olivia Dianara Pita Sllim1 2 Satoshi Ide2

1School of Engineering, National Autonomous University of Mexico, Mexico City, Mexico2Department of Earth and Planetary Sciences, School of Science, University of Tokyo, Tokyo, Japan

Summer 2018

Abstract

In order to determine the influence of different networks in the detection and locationof tremors in Guerrero, cross-correlation between signals was done changing distancebetween stations and their arrangements. It was found that the new temporal SATREPSnetwork improves the possible tremors detection and gives more insight about the differentevents occurring in the region such as SSE and local earthquakes and its aftershocks. Inaddition to this, a relationship between the number of events detected and the number ofstations available was found.

1 Introduction

Nonvolcanic tremors are a weak but persistentshaking of the Earth(Beroza et al., 2011), theywere discovered in the Nankai Through sub-duction zone of Japan in 2002 by KazushigeObara. The main zones where tremors havebeen found are subduction zones. In thesezones, an oceanic plate runs into a continen-tal plate and slides beneath it. This type oftectonic plate interaction is known for earth-quakes that produce tsunamis and are oftenresponsible for volcanic ranges too. Someplaces where tremors have been observed areNankai, New Zeland, Alaska, Costa Rica, Cas-cadia and Mexico.

There are several characteristics that helpdistinguish a tremor from an earthquake. Forexample, the signal to noise ratio is good inearthquakes which makes possible a clear iden-tification of arrivals. In the case of tremors,there is a low signal to noise ratio which makesno possible the identification of P and S wave

arrivals. Also tremors have longer durationcompared to earthquakes.

The zone of study for this project is lo-cated in the pacific coast of Mexico in thestate of Guerrero. This zone is a subduc-tion zone, where the Cocos plate is movingunder the North American plate. There hasnot been significant subduction thrust earth-quake in a zone of the coast of Guerrero sincethe 1900s, receiving the name of Guerrero-Gap,representing an important hazard to thepopulation.

There have been several projects to knowmore about the seismic nature of the region,current networks are the SSN permanent Net-work and the SATREPS project network. TheSATREPS network is funded by the Japanesegovernment and it is a collaboration betweenJapanese and Mexican universities and gov-ernments.

As previous studies suggest the detec-tion and location of tremors depends on thearrangement of the network used(Maury et

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al., 2016). Therefore, data from the newSATREPS network was provided to analyzeand determine how the arrangements of thenew network and its stations affect the detec-tion and location of possible tremors.

Figure 1: Study area in Guerrero, Mexico,showing the Guerrero Gap in yellow, the mainrupture areas and the convergence rate of Co-cos plate. Modified from Radiguet, 2014.

2 Data and Methodology

2.1 Seismic Data

For this project two sets of data from two dif-ferent networks were analyzed and compared.The first set consist of data from the Na-tional Seismological Service of Mexico (SSN)Network, which consists of 63 permanent sta-tions all over Mexico. The second set of datacomes from the SATREPS temporal networkconsisting of 7 stations located in the coastof Guerrero, more specifically in the GuerreoGap. Data from both networks were recordedwith broad band sensors. The data analysiswas done in two different periods, the first onefrom January 2017 to February 2018 and thesecond one from February 2018 to June 2018.

To be able to analyze and use data from bothnetworks, signals were re-sampled to 1 sampleper second.

2.2 Methodology

In order to detect and locate tremors, a cross-correlation between signals from the differentstations was made.

The cross-correlation measures how simi-lar is a signal to another, in this case, howsimilar are signals from one station to signalsfrom another station. To do the correlationbetween signals a code in Fortran was providedwith consists of correlating signals by windowsof 5 minutes with 2.5 minutes of overlapping.To identify and locate possible tremors, therewere some specifications needed in the code,for example: the range for an acceptable cor-relation value, minimum number of correlationdetected and the acceptable range betweenstations. For this project, the minimum corre-lation value was established and 0.6 in at least10 correlations. To determine the influenceof the distance between stations, 100km and200km were compared. When the above re-strictions are true, the code registers and eventand its duration. For these events, 3 impor-tant parameters are needed: latitude, longi-tude and depth. To determine these parame-ters, an inversion is made using the differenceof time of arrival between stations with andS-wave velocity model. From the inversion,the location, depth, magnitude and error isobtained for each event.

The cross-correlation code not only detectstremors, local earthquakes, teleseismic earth-quakes and noise can be set as events. Tobe able to differ tremors from the rest, sev-eral discards were made. The first discardingwas made regarding the duration of the event,tremor have longer duration compared to localsmall events so those with less than 20 secondsof duration were discarded. After that, a com-parison between the detected events and theofficial SSN earthquake catalogue was made to

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discard already known local earthquakes. Tohave better statistics of the possible tremorsdetected in Guerrero, events outside the regionof interest were discarded. Because the codeoverlaps windows of 5 minutes every 2.5 min-utes, for some cases the same signal is detectedtwo times and set as two different events. Inorder to take out the repeated events, a codewas made that keeps the event with less errorin the inversion.

For the discarding of the SSN earthquakes,the events outside the region and the repeatedones, the programming of the codes was madein MATLAB.

The procedure mentioned above was car-ried through with two different distances be-tween stations and with different networks andstations arrangements.

3 Results

3.1 First period: January 18th2017 to February 5th 2018

3.1.1 Varying the networks

As it was already known form (Maury et al.,2016), the detection and location of tremorsare affected by how the stations are arrangedin the area of interest. In order to determinethe performance of the new SATREPS net-work and the permanent SSN network, dif-ferent arrangements of both stations and net-works were compared. For this comparison thedistance between stations was set to 100km.

Figure 2: Location of events for the SATREPSnetwork (a) and SSN network (b) for 100 kmof distance between stations.

Figure 2 shows the possible tremors de-tected by the SSN and the SATREPS net-works during the same amount of time. Itcan be observed that with the SSN networkmuch more events were detected, which was anexpected outcome because more stations wereavailable(Figure 2a). An interesting result wasachieved when both the SSN and SATREPSnetworks were merged. As a result, not onlythe number of events detected increased, an-other tremor occurrence zone can be observedclearly in the -100o W (Figure 3).

Figure 3: Location of events for the SATREPSand SSN networks.

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3.1.2 Varying the number of stations

The number of stations was modified in or-der to determine how the number of stationsnear the interest area affected the detectionson it. For 100 km, more events were detectedwhen increasing the stations in the region andcloser states (Guerrero, Oaxaca, Puebla, Hi-dalgo, Michoacan) and not when using the sta-tions from all Mexico. Not all the detectedevents were located in the region of interest,in this case the arrangement with more de-tections in the region was achieved using thestations in all Mexico.

3.1.3 Increasing the distance betweenstations

When the distance between stations was dou-bled to 200 km, the detections increased sig-nificantly as expected. For example, forthe SATREPS and SSN merged network theevents increased from 1025 to 2022 as it canbe seen in figure 4. But not only the num-ber of detections increased, the average erroralso increased significantly when the distancebetween stations was doubled.

Figure 4: Location of events for the SSN net-work for 100 km (a) 200 km (b) of distancebetween stations.

3.2 Second period: January18th 2017 to February 5th2018

For the second period, the results were ob-tained using a distance of 100km between thestations of SATREPS and SSN networks to-gether. 588 events were detected and 260 ofthose were located in Guerrero(Figure 5).

Figure 5: Location of events for the SATREPSand SSN network for the second period.

3.3 Temporal distribution oftremors

A bar graph was made to observe the tempo-ral distribution of the number of events duringthe 18 months analyzed (Figure 6). From this,a relationship to events such as the 2017 SSEin Guerrero (May-October) or to the Septem-ber 2017 earthquakes can be observed. In the6th and 7th month (June and July) the num-ber of possible tremors increases significantly,the months of the SSE. The number of pos-sible tremors increases again in October, hav-ing a possible relationship to the earthquakesthat occurred in September, from which after-shocks are still happening. The last month(June 2018) has 5 detections because at themoment of analysis not all data were available.

Figure 6: Bar graph of the number of eventsdetected during the 18 months analyzed forthe SATREPS and SSN networks merged.

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3.4 Spatial distribution oftremors

To support that the majority of events de-tected differed form earthquakes, the SSN of-ficial catalogue and the possible tremors wereplotted together (Figure 7). The distributionsof local earthquakes and possible tremors canbe clearly separated which suggest that in thepossible tremors detected local earthquakesare a minority.

Figure 7: Location of possible tremors in redand local earthquakes from the SSN cataloguein blue. Stations of SATREPS and SSN net-works in yellow.

4 Conclusions

For two different periods of time and with dif-ferent arrangements, possible tremors were de-tected and located. When comparing the dif-ferent maps of the different networks, it canbe concluded that the detection and locationof events has a direct relationship with the ar-rangement of the network and the number ofstations. From this, it is of interest to mentionthat the new SATREPS network improves thedetection ability of possible tremors.

Regarding the cross-correlation method,it proved useful to not only detect possibletremors, as some detected events were con-firmed to be local earthquakes when lookingat the waveforms, it can be used to completethe catalogue of local earthquakes.

Interesting information was found whenlooking at the temporal distribution of the

events. The months where the number of de-tections increase significantly, can be relatedto events such as SSE and the September localearthquakes and its aftershocks. These eventscan influence but not completely determinethe number of detections because there are pe-riods of time when there are no data availablefor some stations, specially the SATREPS sta-tions. As already proved, when the number ofstations varies, so does the number of eventsdetected.

From the spatial distribution it can be con-cluded that earthquakes and tremors are com-plementary in the distribution and that thepossible tremors detected have a preference oflocation different from the local earthquakes.Overall, more data are needed to have a betterunderstanding of the different types of eventsin the area.

5 Acknowledgments

I would like to thank Prof. Satoshi Ide and themembers of the Earthquake Science Group fortheir guidance and help during my stay, theother UTRIP participants for the amazing mo-ments together, the International Liaison Of-fice of School of Science for all their help andeffort, and lastly the Graduate School of Sci-ence for the generous scholarship.

References

Beroza, G. C., and S. Ide (2011), Slow Earth-quakes and Nonvolcanic Tremor, AnnualReview of Earth and Planetary Sciences,39(1), 271–296, doi :10.1146/ annurev-earth-040809-152531.

Cruz-Atienza, V. M., A. Husker, D. Legrand,E. Caballero, and V. Kostoglodov (2014),Nonvolcanic tremor locations and mecha-nisms in Guerrero, Mexico, from energy-based and particle motion polarization anal-

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ysis, J. Geophys. Res. Solid Earth, 119,doi:10.1002/2014JB011389.

Cruz-Atienza, V. M.,I. Yoshihiro, V. Kos-toglodov, V. Hjorleifsdottir, A. Iglesias, J.Tago, M. Calo, J. Real, A. Husker, S. Ide,T. Nishimura, M. Shinohara, C. Mortera-Gutierrez, S. Garcıa, M. Kido.A Seismo-geodetic Amphibious Network in the Guer-rero Seismic Gap, Mexico. SeismologicalResearch Letters ; 89 (4): 1435–1449. doi:https://doi.org/10.1785/0220170173

Husker, A. L., V. Kostoglodov, V. M. Cruz-Atienza, D. Legrand, N. M. Shapiro, J. S.Payero, M. Campillo, and E. Huesca- Perez(2012), Temporal variations of non-volcanictremor (NVT) locations in the Mexican sub-duction zone: Finding the NVT sweet spot,Geochem. Geophys. Geosyst., 13, Q03011,doi:10.1029/2011GC003916.

Kostoglodov, V., S. K. Singh, J. A. Santi-ago, S. I. Franco, K. Larson, A. Lowry,and R. Bilham (2003), A large silent earth-quake in the Guerrero seismic gap, Mex-

ico, Geophys. Res. Lett., 30(15), 1807,doi:10.1029/2003GL017219.

Maury, J., S. Ide, V. M. Cruz-Atienza,V.Kostoglodov, G. Gonzalez-Molina, and X.Perez-Campos (2016), Comparative study oftectonic tremor locations: Characterizationof slow earthquakes in Guerrero, Mexico, J.Geophys. Res. Solid Earth, 121, 5136–5151,doi:10.1002/ 2016JB013027.

Maury, J., Ide, S., Cruz-Atienza, V. M., Kos-toglodov, V. (2018). Spatiotemporal varia-tions in slow earthquakes along the Mexi-can subduction zone. Journal of Geophysi-cal Research: Solid Earth, 123, 1559–1575.https://doi.org/10.1002/2017JB014690

Obara K. 2002. Nonvolcanic deep tremor asso-ciated with subduction in southwest Japan.Science 296;1679–81 Payero (2008), Non-volcanic tremor observed in the Mexicansubduction zone, Geo- phys. Res. Lett, 35.

USGS, https://earthquake.usgs.gov/learn/kids/eqscience.php,consulted 27 July 2018.

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