基于数据驱动的时间序列b值计算新方法(TbDD):以2021年云南漾濞MS6.4地震序列为例

doi:10.6038/cjg2021P0385

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Abstract The b-value of time sequence has great potential in application for the seismic hazard analysis of natural earthquakes and industrial-mining induced earthquakes. However, it has long been subject to the subjectivity in the artificial selection of calculation rules, the reliability of calculation results and low accuracy of mutation recognition in time sequence, which restricts the comparability of different results and the refinement of scientific consensus. In this work, we apply the data-driven approach to seismic parameter calculation to construct a new Time-sequence b-value Data Driven method, TbDD for short. We use technologies such as the Ogata-Katsura 1993 (OK1993) model of continuous function, random partition of time axis and model selection based on the Bayesian information criterion (BIC) in TbDD at the same time. We use synthetic earthquake catalogs which are used in TbDD for theoretical verification, and compare the results with those produced by the traditional methods of fixed window length and step length with the same number of earthquakes, the accumulated window and step length with fixed number of earthquakes. The results show that TbDD can well recover the initial input parameter of b₀ when utilizing the synthetic catalogs. It has a significant advantage in the objectivity of calculating rules setting and the accurate identification of the b-value mutation process. We apply the new approach and the corresponding model selection rules to an actual earthquake catalog of the M_S6.4 Yangbi, Yunnan earthquake of May 21, 2021. The results show that the b-value remains around 0.7 before the M_S6.4 main shock and the decline in the amplitude of about 0.1 appears 20 hours before the M_S6.4 main shock, which indicates that the differential stress level of this region is high. The b-value changes obviously after the M_S6.4 main shock, gradually increases to about 0.8, which may be related to the early stress adjustment in the seismic zone after the main shock. Further, we test TbDD by using random models and find that the b-value acquired by TbDD has strong stability and is less affected by the number of random models. When we test TbDD by using random partition of time axis, we find that the settings of random partition of time axis can affect the identification of micro-fluctuations in b-values of time sequence. The TbDD method developed in this paper has a great application potential in the fields with high requirement for calculation accuracy, such as aftershock trend track, and the seismic risk management of industrial-mining induced earthquakes. In the end, the b-value of time sequence calculated from the M_S6.4 Yangbi, Yunnan earthquake in 2021 is of reference value for the understanding of the seismogenic process of the earthquake.

Key words： Data-driven Seismic activity OK1993 model Time sequence analysis b-value

Received: 07 June 2021

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P315

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	JIANG Cong
	JIANG ChangSheng
	YIN FengLing
	ZHANG YanBao
	BI JinMeng
	LONG Feng
	SI ZhengYa
	YIN XinXin

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http://www.geophy.cn//EN/10.6038/cjg2021P0385 OR http://www.geophy.cn//EN/Y2021/V64/I9/3126

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