The use of PhaseNet for Event Identification of Microearthquake Monitoring in Geothermal Field

Muhamad Firdaus Al Hakim; I Putu Raditya Ambara Putra

doi:10.31315/jpgt.v6i1.13437

Authors

Muhamad Firdaus Al Hakim Geophysical Engineering, Faculty of Mineral Technology, UPN Veteran Yogyakarta, Indonesia
I Putu Raditya Ambara Putra Geophysical Engineering, Faculty of Mineral Technology, UPN Veteran Yogyakarta, Indonesia

DOI:

https://doi.org/10.31315/jpgt.v6i1.13437

Abstract

Geothermal energy is a sustainable energy source that requires continuous microseismic monitoring to assess reservoir integrity and geomechanical behavior. Traditional phase identification methods are challenged by noisy environments and complex waveforms, especially in geothermal fields. This study explores the efficacy of PhaseNet, a deep learning neural network model, in detecting P and S wave arrival times for micro-earthquake events. The PhaseNet model was retrained using local seismic data from a geothermal field and tested for its performance in identifying seismic phases. The results were validated against a manual seismic catalog, with additional clustering and association analysis conducted using GaMMA and hypocenter locations determined with NonLinLoc. The findings demonstrate that PhaseNet, combined with GaMMA, provides robust phase detection capabilities, essential for early-stage monitoring in geothermal development.

References

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

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

Folesky, J., Kummerow, J., Shapiro, S. A., Häring, M., & Asanuma, H. (2016). Rupture directivity of fluid‐induced microseismic events: Observations from an enhanced geothermal system. Journal of Geophysical Research: Solid Earth, 121(11), 8034–8047. https://doi.org/10.1002/2016JB013078

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

Huang, W., Wang, R., Li, H., & Chen, Y. (2017). Unveiling the signals from extremely noisy microseismic data for high-resolution hydraulic fracturing monitoring. Scientific Reports, 7(1), 11996. https://doi.org/10.1038/s41598-017-09711-2

Lois, A., Sokos, E., Martakis, N., Paraskevopoulos, P., & Tselentis, G.-A. (2013). A new automatic S-onset detection technique: Application in local earthquake data. GEOPHYSICS, 78(1), KS1–KS11. https://doi.org/10.1190/geo2012-0050.1

Namjesnik, D., Kinscher, J., Gunzburger, Y., Poiata, N., Dominique, P., Bernard, P., & Contrucci, I. (2021). Automatic Detection and Location of Microseismic Events from Sparse Network and Its Application to Post-mining Monitoring. Pure and Applied Geophysics, 178(8), 2969–2997. https://doi.org/10.1007/s00024-021-02773-4

Okamoto, K., Yi, L., Asanuma, H., Okabe, T., Abe, Y., & Tsuzuki, M. (2018). Triggering processes of microseismic events associated with water injection in Okuaizu Geothermal Field, Japan. Earth, Planets and Space, 70(1), 15. https://doi.org/10.1186/s40623-018-0787-7

Permuter, H., Francos, J., & Jermyn, I. (2006). A study of Gaussian mixture models of color and texture features for image classification and segmentation. Pattern Recognition, 39(4), 695–706. https://doi.org/10.1016/j.patcog.2005.10.028

Reynolds, D. A., & Rose, R. C. (1995). Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Transactions on Speech and Audio Processing, 3(1), 72–83. https://doi.org/10.1109/89.365379

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation (pp. 234–241). 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

Ross, Z. E., Trugman, D. T., Azizzadenesheli, K., & Anandkumar, A. (2020). Directivity Modes of Earthquake Populations with Unsupervised Learning. Journal of Geophysical Research: Solid Earth, 125(2). https://doi.org/10.1029/2019JB018299

Rossi, C., Grigoli, F., Cesca, S., Heimann, S., Gasperini, P., Hjörleifsdóttir, V., Dahm, T., Bean, C. J., Wiemer, S., Scarabello, L., Nooshiri, N., Clinton, J. F., Obermann, A., Ágústsson, K., & Ágústsdóttir, T. (2020). Full-Waveform based methods for Microseismic Monitoring Operations: an Application to Natural and Induced Seismicity in the Hengill Geothermal Area, Iceland. Advances in Geosciences, 54, 129–136. https://doi.org/10.5194/adgeo-54-129-2020

Seydoux, L., Balestriero, R., Poli, P., Hoop, M. de, Campillo, M., & Baraniuk, R. (2020). Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning. Nature Communications, 11(1), 3972. https://doi.org/10.1038/s41467-020-17841-x

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

Wang, P., Chang, X., & Zhou, X. (2018). Estimation of the Relative Arrival Time of Microseismic Events Based on Phase-Only Correlation. Energies, 11(10), 2527. https://doi.org/10.3390/en11102527

Wibowo, D. A., Ramadhan, I., Agoes Nugroho, I., Baroek, M. C., Ganefianto, N., Azis, H., Suryantini, Sahara, D. P., & Mozef, P. W. (2022). Microseismic and Focal Mechanism Analyses for Structural Interpretation – Muara Laboh Geothermal Field. IOP Conference Series: Earth and Environmental Science, 1014(1), 012004. https://doi.org/10.1088/1755-1315/1014/1/012004

Yu, Z.-C., Yu, J., Feng, F.-F., Tan, Y.-Y., Hou, G.-T., & He, C. (2020). Arrival picking method for microseismic phases based on curve fitting. Applied Geophysics, 17(3), 453–464. https://doi.org/10.1007/s11770-020-0831-9

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

Zhu, W., McBrearty, I. W., Mousavi, S. M., Ellsworth, W. L., & Beroza, G. C. (2022). Earthquake Phase Association Using a Bayesian Gaussian Mixture Model. Journal of Geophysical Research: Solid Earth, 127(5). https://doi.org/10.1029/2021JB023249