基于机器学习算法的银川台阵资料自动化震相分析和定位

doi:10.12196/j.issn.1000-3274.2021.01.012

Abstract

Abstract: In recent years, the rapid development of machine learning algorithms have significantly improved the accuracy and efficiency of seismic phase picking. In this paper, a hybrid neural network algorithm, which comprised by convolutional neural networks (CNN) and recurrent neural networks (RNN) algorithms, is used to on continuous waveform data recorded by Yinchuan Array in June and July, 2019 for earthquake detection and phase picking. Phases from RNN are associated and located by using the Rapid Earthquake Association and Location method (REAL), and then compared with the routine catalog. The results show that when the number of seismic phases is less than 10, although more events can be detected, the distribution is diffuse, which is inconsistent with the characteristics of regional seismicity. The events with 10 or more seismic phases are manually inspected. In general, as the number of seismic phases increases, the false detection rate is lower. 16 is the critical number of seismic phases to achieve accurate phase detection and location results. When the number of seismic phases is 20 or more, 13 earthquakes in the routine catalog are all recalled. The average location offset between the relocation and routine catalog result is 4.27 km. Besides, the number of manually inspected earthquakes from the automatic procedure is 9 times of that from the routine catalog. It suggests that the procedure which combined the machine learning and REAL method is capable of reducing the difficulty and improving the accuracy and efficiency of earthquake detection.

Key words: Earthquake detection, Phase picking, Phase association, Location

CLC Number:

P315.63

SU Jun, WANG Wei-lai, ZHANG Long, CHEN Ming-fei. Automatic Seismic Phase Analysis and Earthquake Location Using Yinchuan Array Datasets based on a Machine Learning Algorithm[J]. EARTHQUAKE, 2021, 41(1): 153-165.

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Automatic Seismic Phase Analysis and Earthquake Location Using Yinchuan Array Datasets based on a Machine Learning Algorithm

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