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基于机器学习和短周期密集台阵资料研究北流地震余震特征
引用本文:文玺翔, 沈旭章, 周启明. 2022. 基于机器学习和短周期密集台阵资料研究北流地震余震特征. 地球物理学报, 65(9): 3297-3308, doi: 10.6038/cjg2022P0430
作者姓名:文玺翔  沈旭章  周启明
作者单位:1. 中山大学地球科学与工程学院, 广东省地球动力作用与地质灾害重点实验室, 广州 510275; 2. 南方海洋科学与工程广东省实验室(珠海), 广东珠海 519082
基金项目:国家自然基金项目(41874052和41730212),广东省引进人才创业创新团队(2016ZT06N331和2017ZT07Z066),国家重点研发计划(2017YFC1500103),广东省防震减灾协同创新中心(2018B020207011),和第二次青藏高原综合科学考察研究(2019QZKK0701)联合资助
摘    要:

本文基于广西北流5.2级地震震源区震后约30天的短周期密集台阵连续观测资料,利用机器学习方法,对震后余震进行了识别,确定了可靠性较高的441个余震事件,约为同时期固定地震台网目录中余震数量的34倍.进而利用事件波形中P、S波到时信息,对299个余震事件进行了精定位,对信噪比较高的65个地震事件进行了震源机制解反演.根据余震空间分布及震源机制解特征,对该区域中强地震发震构造进行了探讨.结果表明:北流地震的余震主要集中在主震北西约1~3 km的范围内,且大部分余震震源机制解接近于前震;主震的孕震断裂为石窝断层,其走向NWW-SEE,倾角近70°;该区域还存在一条走向NEE-SWW倾角近乎直立的断裂,可能是前震的孕震断层;主震受前震的触发而产生,而后续两条断裂同时处于活动状态,产生了不同震源机制解的余震.此外,在蕉林断裂北端及石窝断裂南端同样拾取到了大量余震事件,这些事件的震源机制解多为逆冲型与走滑型,一致性较差,表明北流地震可能对这两个区域的地震活动起了一个触发作用,但具体触发机制较为复杂.



关 键 词:北流地震   短周期密集台阵   机器学习   微震识别   震源机制解
收稿时间:2021-06-21
修稿时间:2022-05-01

Study on the characters of the aftershocks of Beiliu 5.2 earthquake using machine learning method and dense nodal seismic array
WEN XiXiang, SHEN XuZhang, ZHOU QiMing. 2022. Study on the characters of the aftershocks of Beiliu 5.2 earthquake using machine learning method and dense nodal seismic array. Chinese Journal of Geophysics (in Chinese), 65(9): 3297-3308, doi: 10.6038/cjg2022P0430
Authors:WEN XiXiang  SHEN XuZhang  ZHOU QiMing
Affiliation:1. Guangdong Provincial Key Lab of Geodynamics and Geohazards, School of Earth Sciences and Engineering, Sun Yat-Sen University, Guangzhou 510275, China; 2. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai Guangdong 519082, China
Abstract:In this study, a machine learning method is used to detect the aftershocks of the Guangxi Beiliu 5.2 earthquake based on the 30 days continuous records of dense nodal seismic array. 441 reliable events are detected, which is around 34 times the number from permanent seismic network in the same period. P and S wave arrival time information is used to obtain an earthquake catalog with 299 events. In addition, the focal mechanism solutions of 65 aftershocks with high SNR waveforms are acquired by using the gCAP method. The seismogenic structure of strong earthquakes in this region is discussed based on the spatial distribution of aftershocks and focal mechanism solution characteristics. The results show that the aftershocks of the Beiliu earthquake are mainly concentrated in a range of about 1~3 km northwest of the main shock, and the focal mechanism solutions mostly are closed to the foreshock. The seismogenic fault of the mainshock is the Shiwo fault with a strike of NWW-SEE and a dip angle of nearly 70°. In addition, we speculate that there is a NEE-SWW fault in this region, which may be the seismogenic fault of the foreshock. The mainshock is triggered by the foreshock, and then the two faults are both active and cause a series of aftershocks with different focal mechanism solutions. Some aftershocks are also detected at the northern end of the Jiaolin fault and the southern end of the Shiwo fault. The focal mechanism solutions of these events are mostly of thrust type and strike-slip type with poor consistency, which proves that many faults in these areas began to be active after the Beiliu earthquake. However, the specific trigger mechanism is still indistinct.
Keywords:Beiliu earthquake  Short-period dense nodal seismic array  Machine learning  Earthquake signal detection  Focal mechanism solution
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