Browse > Article
http://dx.doi.org/10.7582/GGE.2022.25.4.177

Machine Learning-based Phase Picking Algorithm of P and S Waves for Distributed Acoustic Sensing Data  

Yonggyu, Choi (Department of Earth Resources and Environmental Engineering, Hanyang University)
Youngseok, Song (Department of Earth Resources and Environmental Engineering, Hanyang University)
Soon Jee, Seol (Department of Earth Resources and Environmental Engineering, Hanyang University)
Joongmoo, Byun (Department of Earth Resources and Environmental Engineering, Hanyang University)
Publication Information
Geophysics and Geophysical Exploration / v.25, no.4, 2022 , pp. 177-188 More about this Journal
Abstract
Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO2 storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.
Keywords
event detection; seismic phase picking; machine learning; distributed acoustic sensing (DAS); transfer learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Allen, R., 1978, Automatic earthquake recognition and timing from single traces: Bull. Seismol. Soc. Amer., 68(5), 1521-1532. https://doi.org/10.1785/BSSA0680051521   DOI
2 Daley, T. M., Miller, D. E., Dodds, K., Cook, P., and Freifeld, B. M., 2016, Field testing of modular borehole monitoring with simultaneous distributed acoustic sensing and geophone vertical seismic profiles at Citronelle, Alabama, Geophys. Prospect., 64(5), 1318-1334. https://doi.org/10.1111/1365-2478.12324   DOI
3 Diakogiannis, F. I., Waldner, F., Caccetta, P., and Wu, C., 2020, ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data, ISPRS J. Photogramm. Remote Sens., 162, 94-114. https://doi.org/10.1016/j.isprsjprs.2020.01.013   DOI
4 Gwon, L., 2016, Review of CO2 Storage Projects and Driving Strategy of CO2 Storage Program in Korea, KEPCO J. Electr. Power Energy, 2(2), 167-165. https://doi.org/10.18770/KEPCO.2016.02.02.167   DOI
5 Huh, D., and Park, Y., 2009, Status of Otway CO2 Storage Project in Australia, J. Geol. Soc. Korea, 45(5), 517-525. https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART001389834
6 KIGAM, 2015, Development of In-situ Monitoring Technology for Detecting Underground Behavior and Leakage of Injected CO2. https://scienceon.kisti.re.kr/commons/util/originalView.do?dbt=TRKO&cn=TRKO201700000433
7 Lomax A., Satriano, C., and Vassallo, M., 2012, Automatic Picker Developments and Optimization: FilterPicker-a Robust, Broadband Picker for Real-Time Seismic Monitoring and Earthquake Early Warning, Seismol. Res. Lett., 83(3), 531-540. https://doi.org/10.1785/gssrl.83.3.531   DOI
8 Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., and Beroza, G. C., 2020, Earthquake transformer-an attentive deep-learning model for simultaneous earthquake detection and phase picking, Nat. Commun., 11, 3592 (2020). https://doi.org/10.1038%2Fs41467-020-17591-w   DOI
9 Mousavi, S. M., Sheng, Y., Zhu, W., and Beroza, G. C., 2019, Stanford Earthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI, IEEE Access, 7, 179464-179476. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8871127   DOI
10 National Research Institute for Earth Science and Disaster Resilience, 2019, NIED K-NET, KiK-net, National Research Institute for Earth Science and Disaster Resilience, doi:10.17598/NIED.0004.   DOI
11 Pan, S. J., and Yang, Q., 2010, A Survey on Transfer Learning, IEEE Trans. Knowl. Data Eng., 22(10), 1345-1359. https://doi.org/10.1109/TKDE.2009.191   DOI
12 Peng, Z., and Zhao, P., 2009, Migration of early aftershocks following the 2004 Parkfield earthquake, Nat. Geosci., 2, 877-881. https://www.nature.com/articles/ngeo697   DOI
13 Ronneberger, O., Fischer, P., and Brox, T., 2015, U-Net: Convolutional Networks for Biomedical Image Segmentation, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, 234-241. https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28
14 Ross, Z. E., Meier, M., Hauksson, E., and Heaton, T. H., 2018, Generalized Seismic Phase Detection with Deep Learning, Bull. Seismol. Soc. Amer., 108(5A), 2894-2901. https://doi.org/10.1785/0120180080   DOI
15 Saragiotis, C. D., Hadjileontiadis, L. J., and Panas, S. M., 2002, PAI-S/K: A robust automatic seismic P phase arrival identification scheme, IEEE Trans Geosci Remote Sens, 40(6), 1395-1404. https://doi.org/10.1109/TGRS.2002.800438   DOI
16 Zhu, W., and Beroza, G. C., 2019, PhaseNet: a deep-neuralnetwork-based seismic arrival-time picking method, Geophys. J. Int., 216(1), 261-273. https://doi.org/10.1093/gji/ggy423   DOI
17 Shelly, D. R., Beroza, G. C., and Ide, S., 2007, Non-volcanic tremor and low-frequency earthquake swarms, Nature, 446, 305-307. https://www.nature.com/articles/nature05666   DOI
18 Silixa, 2022, https://silixa.com/technology/idas-intelligentdistributed-acoustic-sensor/, (October 12, 2022 Accessed)
19 Sleeman, R., and Van Eck, T., 1999, Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings, Phy. Earth and Planet. Inter., 113(1-4), 265-275. https://doi.org/10.1016/S0031-9201(99)00007-2   DOI