The use of PhaseNet and GaMMA in microseismic monitoring in geothermal fields

I Putu Raditya Ambara Putra

Abstract


Due to its potential to decrease greenhouse gas emissions and decrease dependence on fossil fuels, geothermal energy has recently attracted more attention as a renewable and sustainable form of power generation. For evaluating the reservoir's integrity and understanding the underlying geomechanical processes in geothermal fields, microseismic monitoring is crucial. To accurately analyze and understand these microseismic occurrences, it is necessary to accurately identify their phases. Therefore, in this study, we used the PhaseNet-GaMMA combination to identify the arrival times of P and S and associate them to determine the microseismic events of these phases. PhaseNet-GaMMA succeeded in detecting a greater number of phases and events compared to catalog data, where the identification match rate was 85%. Even so, the time required for automatic detection of PhaseNet-GaMMA is relatively short and simple, so it is very good if used as an initial stage in the process of identifying phases and microevents in geothermal fields.

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




DOI: https://doi.org/10.31315/jmtg.v14i2.11436

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