Machine-learning-facilitated earthquake and anthropogenic source detections near the Weiyuan Shale Gas Blocks, Sichuan, China

IF 2.9 3区 地球科学
PengCheng Zhou, William L. Ellsworth, HongFeng Yang, Yen Joe Tan, Gregory C. Beroza, MinHan Sheng, RiSheng Chu
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引用次数: 11

Abstract

Seismic hazard assessment and risk mitigation depend critically on rapid analysis and characterization of earthquake sequences. Increasing seismicity in shale gas blocks of the Sichuan Basin, China, has presented a serious challenge to monitoring and managing the seismicity itself. In this study, to detect events we apply a machine-learning-based phase picker (PhaseNet) to continuous seismic data collected between November 2015 and November 2016 from a temporary network covering the Weiyuan Shale Gas Blocks (SGB). Both P- and S-phases are picked and associated for location. We refine the velocity model by using detected explosions and earthquakes and then relocate the detected events using our new velocity model. Our detections and absolute relocations provide the basis for building a high-precision earthquake catalog. Our primary catalog contains about 60 times as many earthquakes as those in the catalog of the Chinese Earthquake Network Center (CENC), which used only the sparsely distributed permanent stations. We also measure the local magnitude and achieve magnitude completeness of ML0. We relocate clusters of events, showing sequential migration patterns overlapping with horizontal well branches around several well pads in the Wei202 and Wei204 blocks. Our results demonstrate the applicability of a machine-learning phase picker to a dense seismic network. The algorithms can facilitate rapid characterization of earthquake sequences.

Abstract Image

中国四川威远页岩气区块附近的机器学习促进地震和人为震源探测
地震灾害评估和减轻风险关键取决于对地震序列的快速分析和表征。中国四川盆地页岩气区块地震活动性的增加,对地震活动性的监测和管理本身提出了严峻的挑战。在这项研究中,为了检测事件,我们将基于机器学习的相位选择器(PhaseNet)应用于2015年11月至2016年11月期间从覆盖威远页岩气区块(SGB)的临时网络中收集的连续地震数据。P相和s相都被挑选出来并关联起来进行定位。我们通过使用检测到的爆炸和地震来改进速度模型,然后使用我们的新速度模型重新定位检测到的事件。我们的探测和绝对重新定位为建立高精度地震目录提供了基础。我们的主目录所包含的地震数量是中国地震台网中心(CENC)目录中地震数量的60倍左右,后者仅使用稀疏分布的永久台站。我们还测量了ML0的局部震级,实现了其震级完备性。我们重新定位了we202和we204区块几个井台周围水平井分支重叠的连续运移模式。我们的结果证明了机器学习相位选择器在密集地震网络中的适用性。该算法有助于快速表征地震序列。
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来源期刊
Earth and Planetary Physics
Earth and Planetary Physics GEOSCIENCES, MULTIDISCIPLINARY-
自引率
17.20%
发文量
174
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