Automatic earthquake detection and phase picking in Muong Te, Lai Chau region: an application of machine learning in observational seismology in Vietnam

Authors

  • Nguyen Cong Nghia 1-Taiwan International Graduate Program Earth Sciences System (TIGP-ESS), Academia Sinica, Taiwan; 2- Department of Earth Sciences, National Central University, Taiwan
  • Nguyen Van Duong 1- Institute of Geophysics, Vietnam Academy of Science and Technology, Hanoi, Vietnam; 2- Graduate University of Science and Technology (GUST), Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Ha Thi Giang Institute of Geophysics, Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Dinh Quoc Van Institute of Geophysics, Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Nguyen Le Minh 1- Institute of Geophysics, Vietnam Academy of Science and Technology, Hanoi, Vietnam; 2- Graduate University of Science and Technology (GUST), Vietnam Academy of Science and Technology, Hanoi, Vietnam; 3- Department of International Cooperation, Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Bor-Shouh Huang Institute of Earth Sciences, Academia Sinica, Taipei, Taiwan
  • Pham The Truyen Institute of Geophysics, Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Nguyen Tien Hung 1- Institute of Geophysics, Vietnam Academy of Science and Technology, Hanoi, Vietnam; 2- Graduate University of Science and Technology (GUST), Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Le Quang Khoi Institute of Geophysics, Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Nguyen Huu Hung Department of Natural Science Hung Vuong University, Viet Tri, Phu Tho, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/17253

Keywords:

Muong Te earthquake, machine learning, EQ Tranformer, Dien Bien Phu fault, upstream Da river fault, earthquake monitoring

Abstract

We applied the automatic detection and picking of P- and S-wave to one-year continuous raw seismic data from 17 seismic stations in the Muong Te area, northwestern Vietnam. The deep learning picker, Earthquake Transformer, has performed automatic picking of P- and S-waves, and phase association, then we located the earthquakes using Hypoinverse and NonLinLoc programs. The newly derived catalog consisted of 893 events, which is significantly higher than the number of events in the manual catalog. From this new catalog, we can observe more earthquakes related to the Muong Te ML 4.9 earthquake on June 16, 2020, and the earthquake activity in other faults such as the Dien Bien Phu and Muong Nhe faults. The extended catalog can further study the seismogenesis and the seismic velocity structure of the crust beneath northwestern Vietnam.

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References

Allen R.V., 1978. Automatic earthquake recognition and timing from single traces. Bulletin of the seismological society of America, 68, 1521-1532.

Baer M., Kradolfer U., 1987. An automatic phase picker for local and teleseismic events. Bulletin of the Seismological Society of America, 77, 1437-1445.

Baillard C., Crawford W.C., Ballu V., Hibert C., Mangeney A., 2014. An automatic kurtosis‐based P‐and S‐phase picker designed for local seismic networks. Bulletin of the Seismological Society of America, 104, 394-409.

Brodsky E.E., 2019. The importance of studying small earthquakes. Science, 364(6442), 736-737. Doi: 10.1126/science.aax2490.

Cichowicz A., 1993. An automatic S-phase picker. Bulletin of the Seismological Society of America, 83, 180-189.

Cianetti S., Bruni R., Gaviano S., Keir D., Piccinini D., Saccorotti G., Giunchi C., 2021. Comparison of deep learning techniques for the investigation of a seismic sequence: an application to the 2019, Mw 4.5 Mugello (Italy) earthquake. Journal of Geophysical Research: Solid Earth, p.e 2021JB023405.

Havskov, J., Ottemoller, L., 1999. SEISAN earthquake analysis software. Seismological Research Letters, 70, 532-534.

Hochreiter S., Schmidhuber J., 1997. Long short-term memory. Neural Computation, 9, 1735-1780.

Hutton L., Boore D.M., 1987. The ML scale in southern California. Bulletin of the Seismological Society of America, 77, 2074-2094.

Klein F.W., 2002. User's guide to HYPOINVERSE-2000, a Fortran program to solve for earthquake locations and magnitudes. Open-File Report, US Geological Survey. Doi: 10.3133/ofr02171.

Kong Q., Trugman D.T., Ross Z.E., Bianco M.J., Meade B.J., Gerstoft P., 2019. Machine learning in seismology: Turning data into insights. Seismological Research Letters, 90, 3-14.

LeCun Y., Bengio Y., 1995. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks. MIT Press, 3361(10), p.1995.

Lee W.H.K., Lahr J.C., 1972. HYPO71: a computer program for determining hypocenter, magnitude, and first motion pattern of local earthquakes, Open-File Report, US Geological Survey. Doi: 10.3133/ofr72224.

Liu M., Zhang M., Zhu W., Ellsworth W.L., Li H., 2020. Rapid Characterization of the July 2019 Ridgecrest, California, Earthquake Sequence From Raw Seismic Data Using Machine-Learning Phase Picker. Geophysical Research Letters, 47, e2019GL086189.

Lomax A., Michelini A., Curtis A., Meyers R., 2009. Earthquake location, direct, global-search methods. Encyclopedia of Complexity and Systems Science, 5, 2449-2473.

Mousavi S.M., Ellsworth W.L., Zhu W., Chuang L.Y., Beroza G.C., 2020. Earthquake transformer an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature Communications, 11, 1-12.

Mousavi S.M., Sheng Y., Zhu W., Beroza G.C., 2019. STanford EArthquake Dataset (STEAD): A global data set of seismic signals for AI. IEEE Access, 7, 179464-179476.

Münchmeyer J., Woollam J., Rietbrock A., Tilmann F., Lange D., Bornstein T., Diehl T., Giunchi C., Haslinger F., Jozinović D., 2022. Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers. Journal of Geophysical Research: Solid Earth, p.e2021JB023499.

Nguyen H.P., Pham T.T., Nguyen T.N., 2019. Investigation of long-term and short-term seismicity in Vietnam. Journal of Seismology, 23, 951-966.

Ross Z.E., Meier M.A., Hauksson E., Heaton T.H., 2018. Generalized seismic phase detection with deep learning. Bulletin of the Seismological Society of America, 108, 2894-2901.

Ross Z.E., D.T. Trugman, E. Hauksson, P.M. Shearer, 2019. Searching for hidden earthquakes in Southern California. Science, 364, 767-771. Doi: 10.1126/science.aaw6888.

Saragiotis C.D., Hadjileontiadis L.J., Panas S.M., 2002. PAI-S/K: A robust automatic seismic P phase arrival identification scheme. IEEE Transactions on Geoscience and Remote Sensing, 40, 1395-1404.

Sleeman R., Van Eck T., 1999. Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings. Physics of the Earth and Planetary Interiors, 113, 265-275.

Trnkoczy A., 2009. Understanding and parameter setting of STA/LTA trigger algorithm, New Manual of Seismological Observatory Practice (NMSOP). Deutsches GeoForschungsZentrum GFZ, 1-20.

Trugman D.T., Shearer P.M., 2017. GrowClust: A hierarchical clustering algorithm for relative earthquake relocation, with application to the Spanish Springs and Sheldon, Nevada, earthquake sequences. Seismological Research Letters, 88, 379-391.

Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser Ł., Polosukhin I., 2017. Attention is all you need. Advances in Neural Information Processing Systems, 5998-6008.

Waldhauser F., 2001. hypoDD-A Program to Compute Double-Difference Hypocenter Locations, Open-File Report. US Geological Survey.

Doi: 10.3133/ofr01113.

Waldhauser F., Schaff D.P., 2008. Large‐scale relocation of two decades of Northern California seismicity using cross‐correlation and double‐difference methods. Journal of Geophysical Research: Solid Earth, 113(B8). Doi: https://doi.org/10.1029/2007JB005479.

Wang J., Li T., Gu Y.J., Schultz R., Yusifbayov J., Zhang M., 2020. Sequential Fault Reactivation and Secondary Triggering in the March 2019 Red Deer Induced Earthquake Swarm. Geophysical Research Letters, 47, e2020GL090219.

Wiszniowski J., Plesiewicz B., Lizurek G., 2021. Machine learning applied to anthropogenic seismic events detection in Lai Chau reservoir area, Vietnam. Computers & Geosciences, 146, 104628.

Xiao Z., Wang J., Liu C., Li J., Zhao L., Yao Z., 2021. Siamese Earthquake Transformer: A pair‐input deep‐learning model for earthquake detection and phase picking on a seismic array. Journal of Geophysical Research: Solid Earth, 126, e2020JB021444.

Zhao D., 2015. Multiscale seismic tomography. Springer Geophysics Book Series. Doi: 10.1007/978-4-431-55360-1.

Zhou L., Zhao C., Zhang M., Xu L., Cui R., Zhao C., Duan M., Luo J., 2021. Machine-learning-based earthquake locations reveal the seismogenesis of the 2020 Mw 5.0 Qiaojia, Yunnan earthquake. Geophysical Journal International, 228, 1637-1647.

Zhu W., Beroza G.C., 2019. PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216, 261-273.

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Published

2022-07-01

How to Cite

Nguyen Cong, N., Nguyen Van, D., Ha Thi, G., Dinh Quoc, V., Nguyen Le, M., Huang, B.-S. ., Pham The, T., Nguyen Tien, H., Le Quang, K., & Nguyen Huu, H. (2022). Automatic earthquake detection and phase picking in Muong Te, Lai Chau region: an application of machine learning in observational seismology in Vietnam. Vietnam Journal of Earth Sciences. https://doi.org/10.15625/2615-9783/17253

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