Seismicity Analysis of the Beijing Area from an Enhanced Long-Term Earthquake Catalog

Abstract

Beijing, a major central city, is located at the junction of Northeast Asia and North China, bordered by the Taihang Mountains to the west and the Yanshan Mountain Range to the north. In this study, continuous waveform data from 29 seismic stations operated by the China Earthquake Networks Center (CENC) covering the period from 2010 to 2020 are collected to construct an enhanced catalog for the Beijing area through the integration of both template matching and machine learning methodologies. We first selected 1 884 earthquakes with high signal-to-noise ratios (SNRs) as template events from 3 405 earthquakes, utilizing the graphics processing unit-based match and locate (GPU-M&L) for earthquake detection and location, and a total of 10 392 events were detected. Then, DeepDenoiser, a deep learning-based technique for noise reduction, was applied to extract seismic signals from continuous raw data, and we identified 7 516 seismic events, approximately 2.2 times the number listed in the CENC earthquake catalog. We then employed PhaseNet, a deep neural network-based method, to pick seismic arrival-time, and HypoDD for relocation. Repeating earthquakes were identified by a segmented cross-correlation method through both time and frequency domains. Our study obtains an enhanced catalog for the Beijing area, adding approximately 5 000 earthquakes primarily in the 0–1 magnitude range. The distribution of earthquakes in the Beijing region is mainly concentrated in three main clusters, revealing a potential concealed fault in the Jiudu River area and new evidence of a southern extension of the Xiaotangshan-Dongbeiwang fault zone. Our results provide a reliable and comprehensive data foundation for assessing earthquake risks and enhancing disaster prevention and mitigation strategies in the Beijing region.

Keywords

Beijing area / seismicity / GPU-M&L / earthquakes / disasters preventation

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