Effects on a Deep-Learning, Seismic Arrival-Time Picker of Domain-Knowledge Based Preprocessing of Input Seismograms

Authors

  • Anthony Lomax ALomax Scientific, Mouans-Sartoux, France https://orcid.org/0000-0002-7747-5990
  • Matteo Bagagli Dipartimento di Scienze della Terra, Università di Pisa, Via Santa Maria, 56126 Pisa; Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Nazionale Terremoti, Via di Vigna Murata, 605, Roma, Italy https://orcid.org/0000-0001-9409-8667
  • Sonja Gaviano Istituto Nazionale diGeofisica e Vulcanologia, Sezione di Pisa, via Cesare Battisti, 53, Pisa, Italy; Dipartimento di Scienze della Terra, Università degli Studi diFirenze, Via La Pira 4, Florence, Italy; Now at Dipartimento di Scienze della Terra, Università di Pisa, Via Santa Maria, 53, 56126 Pisa, Italy https://orcid.org/0000-0003-3481-2923
  • Spina Cianetti Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Cesare Battisti, 53, Pisa, Italy https://orcid.org/0000-0002-0690-7274
  • Dario Jozinović Swiss Seismological Service (SED), ETH Zurich, Zurich, Switzerland https://orcid.org/0000-0001-7443-3915
  • Alberto Michelini Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Nazionale Terremoti, Via di Vigna Murata, 605, Roma, Italy https://orcid.org/0000-0001-6716-8551
  • Christopher Zerafa Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Cesare Battisti, 53, Pisa, Italy https://orcid.org/0000-0002-7679-4703
  • Carlo Giunchi Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Cesare Battisti, 53, Pisa, Italy https://orcid.org/0000-0002-0174-324X

DOI:

https://doi.org/10.26443/seismica.v3i1.1164

Keywords:

earthquake, arrival-time picker, machine learning, deep learning, domain knowledge

Abstract

Automated seismic arrival picking on large and real-time seismological waveform datasets is fundamental for monitoring and research. Recent, high-performance arrival pickers apply deep-neural-networks to nearly raw seismogram inputs. However, there is a long history of rule-based, automated arrival detection and picking methods that efficiently exploit variations in amplitude, frequency and polarization of seismograms. Here we use this seismological domain-knowledge to transform raw seismograms as input to a deep-learning picker. We preprocess 3-component seismograms into 3-component characteristic functions of a multi-band picker, plus modulus and inclination. We use these five time-series as input instead of raw seismograms to extend the deep-neural-network picker PhaseNet. We compare the original, data-driven PhaseNet and our domain-knowledge PhaseNet (DKPN) after identical training on datasets of different sizes and application to in- and cross-domain test datasets. We find DKPN and PhaseNet show near identical picking performance for in-domain picking, while DKPN outperforms PhaseNet for some cases of cross-domain picking, particularly with smaller training datasets; additionally, DKPN trains faster than PhaseNet. These results show that while the neural-network architecture underlying PhaseNet is remarkably robust with respect to transformations of the input data (e.g. DKPN preprocessing), use of domain-knowledge input can improve picker performance.

References

Akazawa, T. (2004). A technique for automatic detection of onset time of P-and S-phases in strong motion records. Proc. of the 13th World Conf. on Earthquake Engineering. http://www.iitk.ac.in/nicee/wcee/article/13_786.pdf

Allen, R. (1982). Automatic phase pickers: Their present use and future prospects. Bulletin of the Seismological Society of America, 72(6B), S225–S242. https://doi.org/10.1785/bssa07206b0225 DOI: https://doi.org/10.1785/BSSA07206B0225

Allen, R. V. (1978). Automatic earthquake recognition and timing from single traces. Bulletin of the Seismological Society of America, 68(5), 1521–1532. https://doi.org/10.1785/bssa0680051521 DOI: https://doi.org/10.1785/BSSA0680051521

Alvarez, I., Garcia, L., Mota, S., Cortes, G., Benitez, C., & De la Torre, A. (2013). An Automatic P-Phase Picking Algorithm Based on Adaptive Multiband Processing. IEEE Geoscience and Remote Sensing Letters, 10(6), 1488–1492. https://doi.org/10.1109/lgrs.2013.2260720 DOI: https://doi.org/10.1109/LGRS.2013.2260720

Anant, K. S., & Dowla, F. U. (1997). Wavelet transform methods for phase identification in three-component seismograms. Bulletin of the Seismological Society of America, 87(6), 1598–1612. https://doi.org/10.1785/bssa0870061598 DOI: https://doi.org/10.1785/BSSA0870061598

Baer, M., & Kradolfer, U. (1987). An automatic phase picker for local and teleseismic events. Bulletin of the Seismological Society of America, 77(4), 1437–1445. https://doi.org/10.1785/bssa0770041437 DOI: https://doi.org/10.1785/BSSA0770041437

Bagagli, M. (2022). Seismicity and seismic tomography across scales: application to the greater Alpine region [Phdthesis, ETH Zurich]. https://doi.org/10.3929/ETHZ-B-000580361

Bai, C. -y. (2000). Automatic Phase-Detection and Identification by Full Use of a Single Three-Component Broadband Seismogram. Bulletin of the Seismological Society of America, 90(1), 187–198. https://doi.org/10.1785/0119990070 DOI: https://doi.org/10.1785/0119990070

Balestriero, R., & Baraniuk, R. (2018). Mad Max: Affine Spline Insights into Deep Learning. arXiv. https://doi.org/10.48550/ARXIV.1805.06576

Beyreuther, M., Barsch, R., Krischer, L., Megies, T., Behr, Y., & Wassermann, J. (2010). ObsPy: A Python Toolbox for Seismology. Seismological Research Letters, 81(3), 530–533. https://doi.org/10.1785/gssrl.81.3.530 DOI: https://doi.org/10.1785/gssrl.81.3.530

Beyreuther, Moritz, Hammer, C., Wassermann, J., Ohrnberger, M., & Megies, T. (2012). Constructing a Hidden Markov Model based earthquake detector: application to induced seismicity: Constructing a HMM based earthquake detector. Geophysical Journal International, 189(1), 602–610. https://doi.org/10.1111/j.1365-246x.2012.05361.x DOI: https://doi.org/10.1111/j.1365-246X.2012.05361.x

Borghesi, A., Baldo, F., & Milano, M. (2020). Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey. arXiv. https://doi.org/10.48550/ARXIV.2005.10691

Chen, C., & Holland, A. A. (2016). PhasePApy: A Robust Pure Python Package for Automatic Identification of Seismic Phases. Seismological Research Letters, 87(6), 1384–1396. https://doi.org/10.1785/0220160019 DOI: https://doi.org/10.1785/0220160019

Dai, H., & MacBeth, C. (1995). Automatic picking of seismic arrivals in local earthquake data using an artificial neural network. Geophysical Journal International, 120(3), 758–774. https://doi.org/10.1111/j.1365-246x.1995.tb01851.x DOI: https://doi.org/10.1111/j.1365-246X.1995.tb01851.x

Enescu, N. (1996). Seismic Data Processing Using Nonlinear Prediction and Neural networks. IEEE NORSIG Symposium.

Gentili, S., & Michelini, A. (2006). Automatic picking of P and S phases using a neural tree. Journal of Seismology, 10(1), 39–63. https://doi.org/10.1007/s10950-006-2296-6 DOI: https://doi.org/10.1007/s10950-006-2296-6

Hien, D. H. T. (2018). A guide to receptive field arithmetic for Convolutional Neural Networks. https://blog.mlreview.com/a-guide-to-receptive-field-arithmetic-for-convolutional-neural-networks-e0f514068807

Jozinović, D., Lomax, A., Štajduhar, I., & Michelini, A. (2021). Transfer learning: improving neural network based prediction of earthquake ground shaking for an area with insufficient training data. Geophysical Journal International, 229(1), 704–718. https://doi.org/10.1093/gji/ggab488 DOI: https://doi.org/10.1093/gji/ggab488

Kim, A., Nakamura, Y., Yukutake, Y., Uematsu, H., & Abe, Y. (2023). Development of a high-performance seismic phase picker using deep learning in the Hakone volcanic area. Earth, Planets and Space, 75(1). https://doi.org/10.1186/s40623-023-01840-5 DOI: https://doi.org/10.1186/s40623-023-01840-5

Kingma, D. P., & Ba, J. (2017). Adam: A Method for Stochastic Optimization. http://arxiv.org/abs/1412.6980

Kong, Q., Trugman, D. T., Ross, Z. E., Bianco, M. J., Meade, B. J., & Gerstoft, P. (2018). Machine Learning in Seismology: Turning Data into Insights. Seismological Research Letters, 90(1), 3–14. https://doi.org/10.1785/0220180259 DOI: https://doi.org/10.1785/0220180259

Krischer, L., Megies, T., Barsch, R., Beyreuther, M., Lecocq, T., Caudron, C., & Wassermann, J. (2015). ObsPy: a bridge for seismology into the scientific Python ecosystem. Computational Science & Discovery, 8(1), 14003. https://doi.org/10.1088/1749-4699/8/1/014003 DOI: https://doi.org/10.1088/1749-4699/8/1/014003

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539 DOI: https://doi.org/10.1038/nature14539

Liao, W.-Y., Lee, E.-J., Mu, D., Chen, P., & Rau, R.-J. (2021). ARRU Phase Picker: Attention Recurrent‐Residual U‐Net for Picking Seismic P‐ and S‐Phase Arrivals. Seismological Research Letters, 92(4), 2410–2428. https://doi.org/10.1785/0220200382 DOI: https://doi.org/10.1785/0220200382

Lomax, A. J., & Michelini, A. (1988). The use of spherical coordinates in the interpretation of seismograms. Geophysical Journal International, 93(3), 405–412. https://doi.org/10.1111/j.1365-246x.1988.tb03868.x DOI: https://doi.org/10.1111/j.1365-246X.1988.tb03868.x

Lomax, A., Satriano, C., & Vassallo, M. (2012). Automatic Picker Developments and Optimization: FilterPicker–a Robust, Broadband Picker for Real-Time Seismic Monitoring and Earthquake Early Warning. Seismological Research Letters, 83(3), 531–540. https://doi.org/10.1785/gssrl.83.3.531 DOI: https://doi.org/10.1785/gssrl.83.3.531

Lomax, Anthony, Michelini, A., & Curtis, A. (2014). Earthquake Location, Direct, Global-Search Methods. In Encyclopedia of Complexity and Systems Science (pp. 1–33). Springer New York. https://doi.org/10.1007/978-3-642-27737-5_150-2 DOI: https://doi.org/10.1007/978-3-642-27737-5_150-2

Marcus, G. (2018). Innateness, AlphaZero, and Artificial Intelligence. arXiv. https://doi.org/10.48550/ARXIV.1801.05667

McEvilly, T. V., & Majer, E. L. (1982). ASP: An Automated Seismic Processor for microearthquake networks. Bulletin of the Seismological Society of America, 72(1), 303–325. https://doi.org/10.1785/bssa0720010303 DOI: https://doi.org/10.1785/BSSA0720010303

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 DOI: https://doi.org/10.5194/essd-13-5509-2021

Mousavi, S. M., & Beroza, G. C. (2022). Deep-learning seismology. Science, 377(6607). https://doi.org/10.1126/science.abm4470 DOI: https://doi.org/10.1126/science.abm4470

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). https://doi.org/10.1038/s41467-020-17591-w DOI: https://doi.org/10.1038/s41467-020-17591-w

Mousavi, S. M., Langston, C. A., & Horton, S. P. (2016). Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform. GEOPHYSICS, 81(4), V341–V355. https://doi.org/10.1190/geo2015-0598.1 DOI: https://doi.org/10.1190/geo2015-0598.1

Mousavi, S. M., Zhu, W., Sheng, Y., & Beroza, G. C. (2019). CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-45748-1 DOI: https://doi.org/10.1038/s41598-019-45748-1

Mousset, E., Cansi, Y., Crusem, R., & Souchet, Y. (1996). A connectionist approach for automatic labeling of regional seismic phases using a single vertical component seismogram. Geophysical Research Letters, 23(6), 681–684. https://doi.org/10.1029/95gl03811 DOI: https://doi.org/10.1029/95GL03811

Münchmeyer, J., Woollam, J., Rietbrock, A., Tilmann, F., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J., & Soto, H. (2022). Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers. Journal of Geophysical Research: Solid Earth, 127(1). https://doi.org/10.1029/2021jb023499 DOI: https://doi.org/10.1029/2021JB023499

Muralidhar, N., Islam, M. R., Marwah, M., Karpatne, A., & Ramakrishnan, N. (2018, December). Incorporating Prior Domain Knowledge into Deep Neural Networks. 2018 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata.2018.8621955 DOI: https://doi.org/10.1109/BigData.2018.8621955

Ni, Y., Hutko, A., Skene, F., Denolle, M., Malone, S., Bodin, P., Hartog, R., & Wright, A. (2023). Curated Pacific Northwest AI-ready Seismic Dataset. Seismica, 2(1). https://doi.org/10.26443/seismica.v2i1.368 DOI: https://doi.org/10.26443/seismica.v2i1.368

Ning, I. L. C., Swafford, L., Craven, M., Davies, K., Earnest, E., & Thornton, D. (2022, August). Automation of passive seismic processing via machine learning and physics-informed methods. Second International Meeting for Applied Geoscience & Energy. https://doi.org/10.1190/image2022-3750116.1 DOI: https://doi.org/10.1190/image2022-3750116.1

Njirjak, M., Otović, E., Jozinović, D., Lerga, J., Mauša, G., Michelini, A., & Štajduhar, I. (2022). The Choice of Time–Frequency Representations of Non-Stationary Signals Affects Machine Learning Model Accuracy: A Case Study on Earthquake Detection from LEN-DB Data. Mathematics, 10(6), 965. https://doi.org/10.3390/math10060965 DOI: https://doi.org/10.3390/math10060965

Park, Y., Beroza, G. C., & Ellsworth, W. L. (2023). A Mitigation Strategy for the Prediction Inconsistency of Neural Phase Pickers. Seismological Research Letters. https://doi.org/10.1785/0220230003 DOI: https://doi.org/10.1785/0220230003

Plešinger, A., Hellweg, M., & Seidl, D. (1986). Interactive high-resolution polarization analysis of broad-band seismograms. Journal of Geophysics, 59(1), 129–139. https://journal.geophysicsjournal.com/JofG/article/view/203

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (pp. 234–241). Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28 DOI: https://doi.org/10.1007/978-3-319-24574-4_28

Ross, Z. E., & Ben-Zion, Y. (2014). Automatic picking of direct P, S seismic phases and fault zone head waves. Geophysical Journal International, 199(1), 368–381. https://doi.org/10.1093/gji/ggu267 DOI: https://doi.org/10.1093/gji/ggu267

Ross, Zachary E., Meier, M., & Hauksson, E. (2018). P Wave Arrival Picking and First‐Motion Polarity Determination With Deep Learning. Journal of Geophysical Research: Solid Earth, 123(6), 5120–5129. https://doi.org/10.1029/2017jb015251 DOI: https://doi.org/10.1029/2017JB015251

Ross, Zachary E., Meier, M., 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 DOI: https://doi.org/10.1785/0120180080

Satriano, C., Lomax, A., & Zollo, A. (2008). Real-Time Evolutionary Earthquake Location for Seismic Early Warning. Bulletin of the Seismological Society of America, 98(3), 1482–1494. https://doi.org/10.1785/0120060159 DOI: https://doi.org/10.1785/0120060159

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(1–4), 265–275. https://doi.org/10.1016/s0031-9201(99)00007-2 DOI: https://doi.org/10.1016/S0031-9201(99)00007-2

Soto, H., & Schurr, B. (2021). DeepPhasePick: A method for detecting and picking seismic phases from local earthquakes based on highly optimized convolutional and recurrent deep neural networks. Geophysical Journal International. https://doi.org/10.1093/gji/ggab266 DOI: https://doi.org/10.1093/gji/ggab266

Stevenson, P. R. (1976). Microearthquakes at Flathead Lake, Montana: A study using automatic earthquake processing. Bulletin of the Seismological Society of America, 66(1), 61–80. https://doi.org/10.1785/bssa0660010061 DOI: https://doi.org/10.1785/BSSA0660010061

Vassallo, M., Satriano, C., & Lomax, A. (2012). Automatic Picker Developments and Optimization: A Strategy for Improving the Performances of Automatic Phase Pickers. Seismological Research Letters, 83(3), 541–554. https://doi.org/10.1785/gssrl.83.3.541 DOI: https://doi.org/10.1785/gssrl.83.3.541

Vidale, J. E. (1986). Complex polarization analysis of particle motion. Bulletin of the Seismological Society of America, 76(5), 1393–1405. https://doi.org/10.1785/BSSA0760051393

Wang, J., & Teng, T.-L. (1995). Artificial neural network-based seismic detector. Bulletin of the Seismological Society of America, 85(1), 308–319. https://doi.org/10.1785/bssa0850010308 DOI: https://doi.org/10.1785/BSSA0850010308

Withers, M., Aster, R., Young, C., Beiriger, J., Harris, M., Moore, S., & Trujillo, J. (1998). A comparison of select trigger algorithms for automated global seismic phase and event detection. Bulletin of the Seismological Society of America, 88(1), 95–106. https://doi.org/10.1785/bssa0880010095 DOI: https://doi.org/10.1785/BSSA0880010095

Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J., & Soto, H. (2022). SeisBench—A Toolbox for Machine Learning in Seismology. Seismological Research Letters, 93(3), 1695–1709. https://doi.org/10.1785/0220210324 DOI: https://doi.org/10.1785/0220210324

Woollam, J., Rietbrock, A., Bueno, A., & De Angelis, S. (2019). Convolutional Neural Network for Seismic Phase Classification, Performance Demonstration over a Local Seismic Network. Seismological Research Letters, 90(2A), 491–502. https://doi.org/10.1785/0220180312 DOI: https://doi.org/10.1785/0220180312

Yeck, W. L., Patton, J. M., Ross, Z. E., Hayes, G. P., Guy, M. R., Ambruz, N. B., Shelly, D. R., Benz, H. M., & Earle, P. S. (2020). Leveraging Deep Learning in Global 24/7 Real-Time Earthquake Monitoring at the National Earthquake Information Center. Seismological Research Letters, 92(1), 469–480. https://doi.org/10.1785/0220200178 DOI: https://doi.org/10.1785/0220200178

Yu, Z., & Wang, W. (2022). LPPN: A Lightweight Network for Fast Phase Picking. Seismological Research Letters, 93(5), 2834–2846. https://doi.org/10.1785/0220210309 DOI: https://doi.org/10.1785/0220210309

Zhang, H., Thurber, C. H., & Rowe, C. (2003). Automatic P-Wave Arrival Detection and Picking with Multiscale Wavelet Analysis for Single-Component Recordings. Bulletin of the Seismological Society of America, 93(5), 1904–1912. https://doi.org/10.1785/0120020241 DOI: https://doi.org/10.1785/0120020241

Zhu, W., & Beroza, G. C. (2018). PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. Geophysical Journal International. https://doi.org/10.1093/gji/ggy423 DOI: https://doi.org/10.1093/gji/ggy423

Downloads

Additional Files

Published

2024-06-20

How to Cite

Lomax, A., Bagagli, M., Gaviano, S., Cianetti, S., Jozinović, D., Michelini, A., Zerafa, C., & Giunchi, C. (2024). Effects on a Deep-Learning, Seismic Arrival-Time Picker of Domain-Knowledge Based Preprocessing of Input Seismograms. Seismica, 3(1). https://doi.org/10.26443/seismica.v3i1.1164

Issue

Vol. 3 No. 1 (2024)

Section

Articles

License

Copyright (c) 2024 Anthony Lomax, Matteo Bagagli, Sonja Gaviano, Spina Cianetti, Dario Jozinović, Alberto Michelini, Christopher Zerafa, Carlo Giunchi

Creative Commons License

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

Funding data