日本地球惑星科学連合2024年大会

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[E] 口頭発表

セッション記号 S (固体地球科学) » S-SS 地震学

[S-SS04] New trends in data acquisition, analysis and interpretation of seismicity

2024年5月26日(日) 09:00 〜 10:15 303 (幕張メッセ国際会議場)

コンビーナ:Grigoli Francesco(University of Pisa)、Enescu Bogdan(京都大学 大学院 理学研究科 地球惑星科学専攻 地球物理学教室)、青木 陽介(東京大学地震研究所)、内出 崇彦(産業技術総合研究所 地質調査総合センター 活断層・火山研究部門)、座長:Enescu Bogdan(京都大学 大学院 理学研究科 地球惑星科学専攻 地球物理学教室)、内出 崇彦(産業技術総合研究所 地質調査総合センター 活断層・火山研究部門)、青木 陽介(東京大学地震研究所)、Francesco Grigoli(University of Pisa)

10:00 〜 10:15

[SSS04-05] Microseismic Data Analysis for Characterizing the Fracture Network System: A Case Study of the Okuaizu Geothermal Field

*Dian Darisma1Yusuke Mukuhira2Kyosuke Okamoto3Naoki Aoyagi3Takahiko Uchide4Takuya Ishibashi3Hiroshi Asanuma3、Takatoshi Ito2 (1.Graduate School of Environmental Studies, Tohoku University、2.Institute of Fluid Science, Tohoku University、3.Fukushima Renewable Energy Institute, National Institute of Advanced Industrial Science and Technology、4.Geological Survey of Japan, National Institute of Advanced Industrial and Technology)

キーワード:microseismicity, hypocenter location, focal mechanism, principal component analysis, fracture network system

In a geothermal field, microseismicity is used for monitoring subsurface conditions, particularly within a geothermal reservoir. In this study, we use microseismic data to build a fracture network system and extract the detailed position and dimension of fractures. This study uses three years of data (2019-2021) recorded from five surface and four borehole stations at the Okuaizu Geothermal Field. First, we define precise hypocenter location and clustering using Growclust. Clusters are determined based on waveform similarity, with a minimum cross-correlation value of 0.6. The results indicate intense microseismicity near the bottom of the injection well, with clusters identified in the northeast and southern regions relative to the well. Some clusters correlate with local faults oriented in the NNW-SSE direction. Then, we analyze each cluster to define fracture orientation from the focal mechanism and principal component analysis (PCA). We used the P polarity with the S/P ratio as a constraint for a better fault plane solution. Polarities for 2019 and 2020 datasets were determined by applying the deep learning algorithm (Uchide, 2020) for events with magnitude Mw greater than 1, whereas those for 2021 were manually picked. With PCA, we also can extract the fracture dimension of each cluster. The 3rd principal component (PC) is regarded as the normal vector of the fault plane only when the cluster's shape is planer, allowing extraction for strike and dip for each cluster. Clusters with a minimum of 20 events are considered for a detailed orientation view. Fractures are defined if the 3rd PC is less than 10%, and fracture zones are characterized by 3rd PC values in the range of 10%-30% or if the focal mechanism solutions significantly differ from that defined by PCA. Our findings estimate 29 fracture zones (eight with focal mechanism solution), one fracture, and 56 undefined small clusters, revealing a highly complex fracture system in the Okuaizu Geothermal Field. The fracture network system obtained from this study is crucial for robust reservoir modeling because the absence of fracture orientation data can lead to overestimation and errors in simulated fracture planes.