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基于台站空间重合性的密集台阵震相快速拾取
陈依伲1, 李俊1, 汪贞杰1, 赵梦琦1, 俞铁宏1, 李丹宁2, 于俊谊1, 王伟涛3
1.浙江省地震局;2.云南省地震局;3.中国地震局地球物理研究所
摘要:
拾取地震波在密集台阵上的走时是开展体波成像的重要基础,而在地震数目和台站数量较多时,人工拾取方法效率不高。本文利用ChinArray一期数据和云南测震台网空间重合的特点,依据测震台网的的观测报告生成体波震相的概略到时,并用密集台阵记录相应窗口内利用AR-AIC方法对震相到时进行精确拾取。对ChinArray一期350个台站记录到的2011-2013年2006个2级以上地震的处理结果表明,该方法可实现震相的快速识别。利用该方法,密集台阵中可提取的P波震相为718573条,S波为512035条,分别是观测报告中的28倍和22倍。对3级以上地震,76.5% 的P波拾取精度小于0.5秒,70.5%的S波精度小于1秒。对于2-3级地震,76.5%的P波拾取精度小于0.5秒,而69.5%的S波拾取精度小于1秒。该方法也可以Pn/Sn等首波信号进行有效的识别。相关方法可利用固定台站观测报告实现流动台站中地震走时的快速拾取,为后续体波成像研究提供了技术支持。
关键词:  密集台阵  震相拾取  空间重合性
DOI:
分类号:
基金项目:本研究受国家重点研发计划(编号:2018YFC1503200),中国地震局地球物理研究所基本科研业务专项:DQJB19B29,及国家自然基金:41790463,浙江省地震局局科技项目:2019zjj05联合资助。
Quick phase identification for mobile dense array with aid from long time phase records of co-located sparse permanent stations
Chen Yini1, Li Jun1, Wang Zhenjie1, Zhao Mengqiao1, Yu Tiehong1, Li Danning2, Yu Junyi1, Wang Weitao3
1.Zhejiang Earthquake Agency;2.Yunnan Earthquake Agency;3.Institute of Geophysics,China Earthquake Administration
Abstract:
The phase identification and travel time picking are critical for seismic tomography, yet become challenging when the number of stations and earthquake are huge. We here present a method to quickly obtain P and S travel times for mobile dense array for pre-determined earthquakes with aid from long time phase records from co-located permanent stations. The records for 1768 M2+ events between 2011 and 2013 from 350 ChinArray stations deployed in Yunnan Province are processed with an improved AR-AIC method utilizing cumulative envelope and rectlinearity. The reference arrivals are predicted based on phase records from 88 permanent stations with similar spatial coverage, which are further refined with AR-AIC. Totally, 718573 P picks and 512035 S picks are obtained for mobile stations, which are 28 and 22 times those for permanent stations, respectively. By comparing the automatic picks with manual picks for 88 permanent stations, for M3+ events, 81.5% of the P pick errors are are less than 0.5 second and 70.5% of S pick errors are less than 1 seconds. For smaller events, 76.5% P picks errors fall into 0.5 seconds and 69.5% S pick errors are smaller than 1 seconds. Moreover, the Pn and Sn phases are easily discriminated from directly P/S, which show the necessary of combining traditional auto picking and emerging machine learning method.
Key words:  Phase picking  Travel time  Dense array