Deep learning detects uncataloged low-frequency earthquakes across regions

Authors

  • Jannes Münchmeyer ISTerre - Université Grenoble Alpes
  • Sophie Giffard-Roisin
  • Marielle Malfante
  • William B. Frank
  • Piero Poli
  • David Marsan
  • Anne Socquet

DOI:

https://doi.org/10.26443/seismica.v3i1.1185

Keywords:

Low frequency earthquake, deep learning, Earthquake detection

Abstract

Documenting the interplay between slow deformation and seismic ruptures is essential to understand the physics of earthquakes nucleation. However, slow deformation is often difficult to detect and characterize. The most pervasive seismic markers of slow slip are low-frequency earthquakes (LFEs) that allow resolving deformation at minute-scale. Detecting LFEs is hard, due to their emergent onsets and low signal-to-noise ratios, usually requiring region-specific template matching approaches. These approaches suffer from low flexibility and might miss LFEs as they are constrained to sources identified a priori. Here, we develop a deep learning-based workflow for LFE detection and location, modeled after classical earthquake detection with phase picking, phase association, and location. Across three regions with known LFE activity, we detect LFEs from both previously cataloged sources and newly identified sources. Furthermore, the approach is transferable across regions, enabling systematic studies of LFEs in regions without known LFE activity. 

References

Armbruster, J. G., Kim, W.-Y., & Rubin, A. M. (2014). Accurate Tremor Locations from Coherent S and P Waves. Journal of Geophysical Research: Solid Earth, 119(6), 5000–5013. https://doi.org/10.1002/2014JB011133

Bombardier, M., Dosso, S. E., Cassidy, J. F., & Kao, H. (2023). Tackling the Challenges of Tectonic Tremor Localization Using Differential Traveltimes and Bayesian Inversion. Geophysical Journal International, 234(1), 479–493. https://doi.org/10.1093/gji/ggad086

Bostock, M. G., Thomas, A. M., Savard, G., Chuang, L., & Rubin, A. M. (2015). Magnitudes and Moment-Duration Scaling of Low-Frequency Earthquakes beneath Southern Vancouver Island. Journal of Geophysical Research: Solid Earth, 120(9), 6329–6350. https://doi.org/10.1002/2015JB012195

California Institute of Technology and United States Geological Survey Pasadena. (1926). Southern California Seismic Network. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/CI

Caltech. (2007). Meso America Subduction Experiment. International Federation of Digital Seismograph Networks. https://doi.org/10.7909/C3RN35SP

Costantino, G., Giffard-Roisin, S., Radiguet, M., Dalla Mura, M., Marsan, D., & Socquet, A. (2023). Multi-station deep learning on geodetic time series detects slow slip events in Cascadia. Communications Earth & Environment, 4(1), 435. https://doi.org/10.1038/s43247-023-01107-7

Cruz-Atienza, V. M., Tago, J., Villafuerte, C., Wei, M., Garza-Girón, R., Dominguez, L. A., Kostoglodov, V., Nishimura, T., Franco, S., Real, J., & others. (2021). Short-term interaction between silent and devastating earthquakes in Mexico. Nature Communications, 12(1), 2171. https://doi.org/10.1038/s41467-021-22326-6

Dragert, H., Wang, K., & James, T. S. (2001). A silent slip event on the deeper Cascadia subduction interface. Science, 292(5521), 1525–1528. https://doi.org/10.1126/science.1060152

Frank, W. B., & Shapiro, N. M. (2014). Automatic Detection of Low-Frequency Earthquakes (LFEs) Based on a Beamformed Network Response. Geophysical Journal International, 197(2), 1215–1223. https://doi.org/10.1093/gji/ggu058

Frank, William B, & Brodsky, E. E. (2019). Daily measurement of slow slip from low-frequency earthquakes is consistent with ordinary earthquake scaling. Science Advances, 5(10), eaaw9386. https://doi.org/10.1126/sciadv.aaw9386

Frank, William B., Shapiro, N. M., Husker, A. L., Kostoglodov, V., Romanenko, A., & Campillo, M. (2014). Using Systematically Characterized Low-Frequency Earthquakes as a Fault Probe in Guerrero, Mexico. Journal of Geophysical Research: Solid Earth, 119(10), 7686–7700. https://doi.org/10.1002/2014JB011457

Geological Survey of Canada. (2000). Portable Observatories for Lithospheric Analysis and Research Investigating Seismicity (POLARIS). International Federation of Digital Seismograph Networks. https://fdsn.org/networks/detail/PO/

Gombert, B., & Hawthorne, J. C. (2023). Rapid Tremor Migration During Few Minute-Long Slow Earthquakes in Cascadia. Journal of Geophysical Research: Solid Earth, 128(2), e2022JB025034. https://doi.org/10.1029/2022JB025034

González-Vidal, D., Moreno, M., Sippl, C., Baez, J. C., Ortega-Culaciati, F., Lange, D., Tilmann, F., Socquet, A., Bolte, J., Hormazabal, J., & others. (2023). Relation between oceanic plate structure, patterns of interplate locking and microseismicity in the 1922 Atacama seismic gap. Geophysical Research Letters, 50(15), e2023GL103565. https://doi.org/10.1029/2023GL103565

Ide, S., Beroza, G. C., Shelly, D. R., & Uchide, T. (2007). A Scaling Law for Slow Earthquakes. Nature, 447(7140), 76–79. https://doi.org/10.1038/nature05780

Ide, S., Shelly, D. R., & Beroza, G. C. (2007). Mechanism of deep low frequency earthquakes: Further evidence that deep non-volcanic tremor is generated by shear slip on the plate interface. Geophysical Research Letters, 34(3). https://doi.org/10.1029/2006GL028890

Imanishi, K., Uchide, T., & Takeda, N. (2016). Determination of focal mechanisms of nonvolcanic tremor using S wave polarization data corrected for the effects of anisotropy. Geophysical Research Letters, 43(2), 611–619. https://doi.org/10.1002/2015GL067249

IRIS Transportable Array. (2003). USArray Transportable Array. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/TA

Japan Meteorological Agency. (2023). The Seismological Bulletin of Japan. http://www.data.jma.go.jp/svd/eqev/data/bulletin/index_e.html

Kao, H., Shan, S.-J., Dragert, H., Rogers, G., Cassidy, J. F., & Ramachandran, K. (2005). A Wide Depth Distribution of Seismic Tremors along the Northern Cascadia Margin. Nature, 436(7052), 841–844. https://doi.org/10.1038/nature03903

Lomax, A., Virieux, J., Volant, P., & Berge-Thierry, C. (2000). Probabilistic earthquake location in 3D and layered models: Introduction of a Metropolis-Gibbs method and comparison with linear locations. Advances in Seismic Event Location, 101–134. https://doi.org/10.1007/978-94-015-9536-0_5

Lowry, A. R., Larson, K. M., Kostoglodov, V., & Bilham, R. (2001). Transient fault slip in Guerrero, southern Mexico. Geophysical Research Letters, 28(19), 3753–3756. https://doi.org/10.1029/2001GL013238

Michel, S., Gualandi, A., & Avouac, J.-P. (2019). Interseismic coupling and slow slip events on the Cascadia megathrust. Pure and Applied Geophysics, 176, 3867–3891. https://doi.org/10.1007/s00024-018-1991-x

Michelini, A., Cianetti, S., Gaviano, S., Giunchi, C., Jozinović, D., & Lauciani, V. (2021). INSTANCE–the Italian seismic dataset for machine learning. Earth System Science Data, 13(12), 5509–5544. https://doi.org/10.5194/essd-13-5509-2021

Mouchon, C., Frank, W. B., Radiguet, M., Poli, P., & Cotte, N. (2023). Subdaily Slow Fault Slip Dynamics Captured by Low-Frequency Earthquakes. AGU Advances, 4(4), e2022AV000848. https://doi.org/10.1029/2022AV000848

Moutote, L., Itoh, Y., Lengliné, O., Duputel, Z., & Socquet, A. (2023). Evidence of a transient aseismic slip driving the 2017 Valparaiso earthquake sequence, from foreshocks to aftershocks. Journal of Geophysical Research: Solid Earth, e2023JB026603. https://doi.org/10.1029/2023JB026603

Münchmeyer, J. (2024). PyOcto: A high-throughput seismic phase associator. Seismica, 3(1). https://doi.org/10.26443/seismica.v3i1.1130

Münchmeyer, J., Woollam, J., Rietbrock, A., Tilmann, F., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., & others. (2022). Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers. Journal of Geophysical Research: Solid Earth, 127(1), e2021JB023499. https://doi.org/10.1029/2021JB023499

Natural Resources Canada. (1975). Canadian National Seismograph Network. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/CN

Natural Resources Canada. (2002). Canadian Seismic Research Network. International Federation of Digital Seismograph Networks. https://fdsn.org/networks/detail/C8/

Northern California Earthquake Data Center. (2014a). Berkeley Digital Seismic Network (BDSN). International Federation of Digital Seismograph Networks. https://doi.org/10.7932/BDSN

Northern California Earthquake Data Center. (2014b). High Resolution Seismic Network (HRSN). International Federation of Digital Seismograph Networks. https://doi.org/10.7932/HRSN

Okada, Y., Nishimura, T., Tabei, T., Matsushima, T., & Hirose, H. (2022). Development of a detection method for short-term slow slip events using GNSS data and its application to the Nankai subduction zone. Earth, Planets and Space, 74(1), 1–18. https://doi.org/10.1186/s40623-022-01576-8

Ozawa, S., Murakami, M., Kaidzu, M., Tada, T., Sagiya, T., Hatanaka, Y., Yarai, H., & Nishimura, T. (2002). Detection and monitoring of ongoing aseismic slip in the Tokai region, central Japan. Science, 298(5595), 1009–1012. https://doi.org/10.1126/science.1076780

Radiguet, M., Perfettini, H., Cotte, N., Gualandi, A., Valette, B., Kostoglodov, V., Lhomme, T., Walpersdorf, A., Cabral Cano, E., & Campillo, M. (2016). Triggering of the 2014 M w 7.3 Papanoa earthquake by a slow slip event in Guerrero, Mexico. Nature Geoscience, 9(11), 829–833. https://doi.org/10.1038/ngeo2817

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, 234–241. https://doi.org/10.1007/978-3-319-24574-4_28

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(5A), 2894–2901. https://doi.org/10.1785/0120180080

Royer, A., & Bostock, M. (2014). A comparative study of low frequency earthquake templates in northern Cascadia. Earth and Planetary Science Letters, 402, 247–256. https://doi.org/10.1016/j.epsl.2013.08.040

Rubin, A. M., & Armbruster, J. G. (2013). Imaging Slow Slip Fronts in Cascadia with High Precision Cross-Station Tremor Locations. Geochemistry, Geophysics, Geosystems, 14(12), 5371–5392. https://doi.org/10.1002/2013GC005031

Savard, G., & Bostock, M. G. (2015). Detection and Location of Low-Frequency Earthquakes Using Cross-Station Correlation. Bulletin of the Seismological Society of America, 105(4), 2128–2142. https://doi.org/10.1785/0120140301

Shelly, D. R. (2010). Migrating tremors illuminate complex deformation beneath the seismogenic San Andreas fault. Nature, 463(7281), 648–652. https://doi.org/10.1038/nature08755

Shelly, D. R. (2017). A 15 Year Catalog of More than 1 Million Low-Frequency Earthquakes: Tracking Tremor and Slip along the Deep San Andreas Fault. Journal of Geophysical Research: Solid Earth, 122(5), 3739–3753. https://doi.org/10.1002/2017JB014047

Shelly, D. R., Beroza, G. C., & Ide, S. (2007). Non-Volcanic Tremor and Low-Frequency Earthquake Swarms. Nature, 446(7133), 305–307. https://doi.org/10.1038/nature05666

Shelly, D. R., Ellsworth, W. L., Ryberg, T., Haberland, C., Fuis, G. S., Murphy, J., Nadeau, R. M., & Bürgmann, R. (2009). Precise Location of San Andreas Fault Tremors near Cholame, California Using Seismometer Clusters: Slip on the Deep Extension of the Fault? Geophysical Research Letters, 36(1). https://doi.org/10.1029/2008GL036367

Socquet, A., Valdes, J. P., Jara, J., Cotton, F., Walpersdorf, A., Cotte, N., Specht, S., Ortega-Culaciati, F., Carrizo, D., & Norabuena, E. (2017). An 8 Month Slow Slip Event Triggers Progressive Nucleation of the 2014 Chile Megathrust. Geophysical Research Letters, 44(9), 4046–4053. https://doi.org/10.1002/2017GL073023

Supino, M., Poiata, N., Festa, G., Vilotte, J. P., Satriano, C., & Obara, K. (2020). Self-Similarity of Low-Frequency Earthquakes. Scientific Reports, 10(1), 6523. https://doi.org/10.1038/s41598-020-63584-6

Tan, Y. J., Waldhauser, F., Ellsworth, W. L., Zhang, M., Zhu, W., Michele, M., Chiaraluce, L., Beroza, G. C., & Segou, M. (2021). Machine-learning-based high-resolution earthquake catalog reveals how complex fault structures were activated during the 2016–2017 Central Italy sequence. The Seismic Record, 1(1), 11–19. https://doi.org/10.1785/0320210001

UNAVCO, United States of America. (2004). Plate Boundary Observatory Borehole Seismic Network (PBO). International Federation of Digital Seismograph Networks. https://fdsn.org/networks/detail/PB/

USGS Menlo Park. (1966). USGS Northern California Seismic Network. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/NC

Wang, Q.-Y., Frank, W. B., Abercrombie, R. E., Obara, K., & Kato, A. (2023). What makes low-frequency earthquakes low frequency. Science Advances, 9(32), eadh3688. https://doi.org/10.1126/sciadv.adh3688

Wech, A. G. (2021). Cataloging Tectonic Tremor Energy Radiation in the Cascadia Subduction Zone. Journal of Geophysical Research: Solid Earth, 126(10), e2021JB022523. https://doi.org/10.1029/2021JB022523

Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., & others. (2022). SeisBench—A toolbox for machine learning in seismology. Seismological Research Letters, 93(3), 1695–1709. https://doi.org/10.1785/0220210324

Zhu, W., & Beroza, G. C. (2019). PhaseNet: A deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261–273. https://doi.org/10.1093/gji/ggy423

Downloads

Additional Files

Published

2024-05-10

How to Cite

Münchmeyer, J., Giffard-Roisin, S., Malfante, M., Frank, W., Poli, P., Marsan, D., & Socquet, A. (2024). Deep learning detects uncataloged low-frequency earthquakes across regions. Seismica, 3(1). https://doi.org/10.26443/seismica.v3i1.1185

Issue

Vol. 3 No. 1 (2024)

Section

Articles

License

Copyright (c) 2024 Jannes Münchmeyer, Sophie Giffard-Roisin, Marielle Malfante, William B. Frank, Piero Poli, David Marsan, Anne Socquet

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Funding data