High-resolution monitoring of hydraulically induced acoustic emission activities using neural phase picking and matched filter analysis

Makoto Naoi; Shiro Hirano; Youqing Chen

doi:10.1186/s40645-025-00696-5

journal article Open Access Mar 21, 2025

High-resolution monitoring of hydraulically induced acoustic emission activities using neural phase picking and matched filter analysis

Makoto Naoi

Shiro Hirano Youqing Chen

Progress in Earth and Planetary Science Vol. 12 No. 1 · Springer Science and Business Media LLC

View at Publisher Save 10.1186/s40645-025-00696-5

Abstract

Abstract
Monitoring the activities of very small seismic events or acoustic emissions (AEs) by estimating their hypocenters is useful in investigating fracturing processes in laboratory experiments. Here, we proposed an analysis procedure to develop high-quality AE event catalogs using deep learning and similar waveform searches from the continuous records of AE sensors. The proposed routine comprised the following five steps: 1) automatically developing catalogs using a conventional procedure, where the short-term average-to-long-term average ratio detects transient signals, and arrival times are identified using an autoregressive model and the Akaike information criterion; 2) training a deep learning model for arrival time reading (neural phase picker) using datasets based on the Step 1 catalog; 3) reproducing the AE catalog by applying the trained neural phase picker to continuous waveform records; 4) applying template matching to continuous waveform records based on the template events listed in the catalog in Step 3; and 5) determining the precise hypocenters of template events and newly detected events in Step 4 using a relative location method based on the cross-correlation travel time reading technique. We applied this procedure to continuous AE waveforms recorded at 10 MHz sampling during hydraulic fracturing experiments, resulting in a catalog with 10 times the number of events compared to the Step 1 catalog. This reproduced catalog revealed new aspects of the fracturing process, such as the propagating fracture front and tremor-like AE activity. The proposed procedure eliminates the need for manual labeling, thereby facilitating a fully automated analysis of the observed continuous records. This technique is expected to enhance our understanding of AE sensor records in laboratory experiments.

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References

37

[1]

Allen RV (1978) Automatic earthquake recognition and timing from single traces. Bull Seismol Soc Am 68:1521–1532. https://doi.org/10.1785/BSSA0680051521 10.1785/bssa0680051521

[2]

Aso N, Ohta K, Ide S (2011) Volcanic-like low-frequency earthquakes beneath Osaka Bay in the absence of a volcano. Geophys Res Lett. https://doi.org/10.1029/2011gl046935 10.1029/2011gl046935

[3]

Chai C, Maceira M, Santos-Villalobos HJ, Venkatakrishnan SV, Schoenball M, Zhu W, Beroza GC, Thurber C, EGS Collab Team (2020) Using a deep neural network and transfer learning to bridge scales for seismic phase picking. Geophys Res Lett 47:e2020GL088651. https://doi.org/10.1029/2020GL088651 10.1029/2020gl088651

[4]

Chen Y, Naoi M, Tomonaga Y, Akai T, Tanaka H, Takagi S, Ishida T (2018) Method for visualizing fractures induced by laboratory-based hydraulic fracturing and its application to shale samples. Energies 11:1976. https://doi.org/10.3390/en11081976 10.3390/en11081976

[5]

Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: International conference on knowledge discovery and data mining (KDD)

[6]

Gibbons SJ, Ringdal F (2006) The detection of low magnitude seismic events using array-based waveform correlation. Geophys J Int 165:149–166. https://doi.org/10.1111/j.1365-246X.2006.02865.x 10.1111/j.1365-246x.2006.02865.x

[7]

Hendriyana A, Tsuji T (2021) Influence of structure and pore pressure of plate interface on tectonic tremor in the Nankai subduction zone. Jpn Earth Planet Sci Lett 558:116742. https://doi.org/10.1016/j.epsl.2021.116742 10.1016/j.epsl.2021.116742

[8]

Hirano S, Kawakata H, Doi I (2022) A matched-filter technique with an objective threshold. Sci Rep 12:22090. https://doi.org/10.1038/s41598-022-25839-2 10.1038/s41598-022-25839-2

[9]

Kato A (2024) Implications of fault-valve behavior from immediate aftershocks following the 2023 Mj6.5 earthquake beneath the Noto Peninsula, central Japan. Geophys Res Lett. https://doi.org/10.1029/2023gl106444 10.1029/2023gl106444

[10]

Kato A, Obara K, Igarashi T, Tsuruoka H, Nakagawa S, Hirata N (2012) Propagation of slow slip leading up to the 2011 M(w) 9.0 Tohoku-Oki earthquake. Science 335:705–708. https://doi.org/10.1126/science.1215141 10.1126/science.1215141

[11]

Kubo H, Naoi M, Kano M (2024) Recent advances in earthquake seismology using machine learning. Earth Planets Space 76:1–32. https://doi.org/10.1186/s40623-024-01982-0 10.1186/s40623-024-01982-0

[12]

Lei X, Masuda K, Nishizawa O, Jouniaux L, Liu L, Ma W, Satoh T, Kusunose K (2004) Detailed analysis of acoustic emission activity during catastrophic fracture of faults in rock. J Struct Geol 26:247–258. https://doi.org/10.1016/s0191-8141(03)00095-6 10.1016/s0191-8141(03)00095-6

[13]

Li Z, Zhu L, Officer T, Shi F, Yu T, Wang Y (2022) A machine-learning-based method of detecting and picking the first P-wave arrivals of acoustic emission events in laboratory experiments. Geophys J Int 230:1818–1823. https://doi.org/10.1093/gji/ggac148 10.1093/gji/ggac148

[14]

Mousavi SM, Beroza GC (2023) Machine learning in earthquake seismology. Annu Rev Earth Planet Sci 51:105–129. https://doi.org/10.1146/annurev-earth-071822-100323 10.1146/annurev-earth-071822-100323

[15]

Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking

S. Mostafa Mousavi, William L. Ellsworth, Weiqiang Zhu et al.

Nature Communications 2020 10.1038/s41467-020-17591-w

[16]

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. J Geophys Res [solid Earth]. https://doi.org/10.1029/2021jb023499 10.1029/2021jb023499

[17]

Naoi M (2018) Study of earthquake generation process by acoustic emission observation in deep gold mines in South Africa. Jishin 71:43–62. https://doi.org/10.4294/zisin.2017-13 10.4294/zisin.2017-13

[18]

Naoi M, Hirano S (2023) Efficient similar waveform search using short binary codes obtained through a deep hashing technique. Geophys J Int 237:604–621. https://doi.org/10.1093/gji/ggae061 10.1093/gji/ggae061

[19]

Naoi M, Nakatani M, Moriya H, Yabe Y (2016) Acoustic emission monitoring for mitigating seismic risks in deep gold mines in South Africa. Int J the JSRM 12:19–22. https://doi.org/10.11187/ijjsrm.12.1_19 10.11187/ijjsrm.12.1_19

[20]

Naoi M, Chen Y, Nishihara K, Yamamoto K, Yano S, Watanabe S, Morishige Y, Kawakata H, Akai T, Kurosawa I, Ishida T (2018) Monitoring hydraulically-induced fractures in the laboratory using acoustic emissions and the fluorescent method. Int J Rock Mech Min Sci 104(1997):53–63. https://doi.org/10.1016/j.ijrmms.2018.02.015 10.1016/j.ijrmms.2018.02.015

[21]

Naoi M, Chen Y, Yamamoto K, Morishige Y, Imakita K, Tsutumi N, Kawakata H, Ishida T, Tanaka H, Arima Y, Kitamura S, Hyodo D (2020) Tensile-dominant fractures observed in hydraulic fracturing laboratory experiment using eagle ford shale. Geophys J Int 222:769–780. https://doi.org/10.1093/gji/ggaa183 10.1093/gji/ggaa183

[22]

Naoi M, Imakita K, Chen Y, Yamamoto K, Tanaka R, Kawakata H, Ishida T, Fukuyama E, Arima Y (2022) Source parameter estimation of acoustic emissions induced by hydraulic fracturing in the laboratory. Geophys J Int 231:408–425. https://doi.org/10.1093/gji/ggac202 10.1093/gji/ggac202

[23]

Obara K (2002) Nonvolcanic deep tremor associated with subduction in southwest Japan. Science 296:1679–1681. https://doi.org/10.1126/science.1070378 10.1126/science.1070378

[24]

Okamoto K, Mukuhira Y, Darisma D, Asanuma H, Moriya H (2024) Machine learning automatic picker for geothermal microseismicity analysis for practical procedure to reveal fine reservoir structures. Geothermics 116:102832. https://doi.org/10.1016/j.geothermics.2023.102832 10.1016/j.geothermics.2023.102832

[25]

Generalized Seismic Phase Detection with Deep Learning

Zachary E. Ross, Men‐Andrin Meier, Egill Hauksson et al.

Bulletin of the Seismological Society of America 2018 10.1785/0120180080

[26]

Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces

Rainer Storn, Kenneth Price

Journal of Global Optimization 1997 10.1023/a:1008202821328

[27]

Sun H, Ross ZE, Zhu W, Azizzadenesheli K (2023) Phase neural operator for multi-station picking of seismic arrivals. Geophys Res Lett. https://doi.org/10.1029/2023gl106434 10.1029/2023gl106434

[28]

Takanami T, Kitagawa G (1988) A new efficient procedure for the estimation of onset times of seismic wave. J Phys Earth 36:267–290. https://doi.org/10.4294/jpe1952.36.267 10.4294/jpe1952.36.267

[29]

Tamaribuchi K, Hirose F, Noda A, Iwasaki Y, Iwakiri K, Ueno H (2021) Noise classification for the unified earthquake catalog using ensemble learning: the enhanced image of seismic activity along the Japan Trench by the S-net seafloor network. Earth Planets Space 73:1–19. https://doi.org/10.1186/s40623-021-01411-6 10.1186/s40623-021-01411-6

[30]

Tanaka R, Naoi M, Chen Y, Yamamoto K, Imakita K, Tsutsumi N, Shimoda A, Hiramatsu D, Kawakata H, Ishida T, Fukuyama E, Tanaka H, Arima Y, Kitamura S, Hyodo D (2021) Preparatory acoustic emission activity of hydraulic fracture in granite with various viscous fluids revealed by deep learning technique. Geophys J Int 226:493–510. https://doi.org/10.1093/gji/ggab096 10.1093/gji/ggab096

[31]

Trugman DT, McBrearty IW, Bolton DC, Guyer RA, Marone C, Johnson PA (2020) The spatiotemporal evolution of granular microslip precursors to laboratory earthquakes. Geophys Res Lett. https://doi.org/10.1029/2020gl088404 10.1029/2020gl088404

[32]

SciPy 1.0: fundamental algorithms for scientific computing in Python

Pauli Virtanen, Ralf Gommers, Travis E. Oliphant et al.

Nature Methods 2020 10.1038/s41592-019-0686-2

[33]

A Double-Difference Earthquake Location Algorithm: Method and Application to the Northern Hayward Fault, California

F. Waldhauser

Bulletin of the Seismological Society of America 2000 10.1785/0120000006

[34]

SeisBench—A Toolbox for Machine Learning in Seismology

Jack Woollam, Jannes Münchmeyer, Frederik Tilmann et al.

Seismological Research Letters 2022 10.1785/0220210324

[35]

Yamashita F, Fukuyama E, Xu S, Kawakata H, Mizoguchi K, Takizawa S (2021) Two end-member earthquake preparations illuminated by foreshock activity on a meter-scale laboratory fault. Nat Commun 12:4302. https://doi.org/10.1038/s41467-021-24625-4 10.1038/s41467-021-24625-4

[36]

Zhang M, Ellsworth WL, Beroza GC (2019) Rapid earthquake association and location. Seismol Res Lett 90:2276–2284. https://doi.org/10.1785/0220190052 10.1785/0220190052

[37]

Zhu W, Beroza GC (2019) PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophys J Int 216:261–273. https://doi.org/10.1093/gji/ggy423 10.1093/gji/ggy423

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Details

Published: Mar 21, 2025
Vol/Issue: 12(1)
License: View

Authors

Funding

JSPS KAKENHI Award: JP16H04614

Kyoto University Foundation

Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan under the Earthquake and Volcano Hazards Observation and Research Program

Cite This Article

Makoto Naoi, Shiro Hirano, Youqing Chen (2025). High-resolution monitoring of hydraulically induced acoustic emission activities using neural phase picking and matched filter analysis. Progress in Earth and Planetary Science, 12(1). https://doi.org/10.1186/s40645-025-00696-5

High-resolution monitoring of hydraulically induced acoustic emission activities using neural phase picking and matched filter analysis

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