白鹤滩水库库区基于深度学习的震相拾取与地震定位

doi:10.12196/j.issn.1000-3274.2023.01.011

Abstract

Abstract: In recent years, deep learning technology has become increasingly prevalent in seismic phase picking and earthquake location research. This paper employs the EQTransformer, a deep neural network, to pick P and S arrivals from continuous data recorded by 34 digital seismic stations in the Baihetan reservoir area from 2016 to 2018. Phase association and preliminary localization by REAL, followed by optimization of earthquake location using VELEST and hypoDD. The research reveals that seismic phase picking based on deep learning is significantly more efficient than traditional manual methods in the Baihetan Reservoir area. The accuracy of the EQTransformer-picked P and S first-arrivals is comparable to that of manually picked phases, with average time differences of 0.03 s and 0.07 s, conforming to a normal distribution. The number of events after preliminary location by REAL (13815) is nearly twice of the routine catalog (7862), and we ulfimately obtain high-precision locations of 7108 earthquakes by the hypoDD. The estimated magnitude is on average 0.27 lower than that in the routine catalog, and the magnitude difference is primavily within 0.7. The minimum magnitude of completeness is changed from M_L1.4 in the routine catalog to M_L0.6+0.27, effectively filling the magnitude gap of the routine catalog and enriching the data on moderate and small earthquakes in the Baihetan reservoir area.

Key words: Phase picking, Earthquake location, Deep learning, The Baihetan reservior

CLC Number:

TP181
P315.7

ZHANG Yu-cheng, HUA Wei. Phase Picking and Earthquake Location based on Deep Learning in the Baihetan Reservior Area[J]. EARTHQUAKE, 2023, 43(1): 137-151.

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Phase Picking and Earthquake Location based on Deep Learning in the Baihetan Reservior Area

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