Time-frequency characteristic analyses of measured and artificial seismic waves using the XGBoost algorithm

doi:10.16511/j.cnki.qhdxxb.2022.25.036

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Abstract The rapid development of artificial intelligence and machine learning techniques that can predict and generalize data sets are gradually being introduced into many engineering projects. The XGBoost artificial intelligence method was used here to analyze the time-frequency characteristics of measured and artificially simulated seismic waves to resolve the two problems of the lack of data and the lack of understanding seismic waves. The advantage of the XGBoost method is that high-performance artificial intelligence methods can analyze large amounts of data that would be difficult by traditional calculational methods. The method can then be used to analyze the time-frequency domain characteristics of seismic waves. The algorithm accurately discriminated 91% of the measured and artificial SIMQKE seismic waves. Further research showed that the difference between the artificial seismic waves and the measured waves was mainly reflected in the correlation of the time-frequency domain characteristics. This study reveals the time-frequency characteristics of seismic waves that will facilitate the development of artificial seismic wave simulation methods.

Keywords artificial seismic waves XGBoost intelligent monitoring SIMQKE wavelet packet analyses

Just Accepted Date: 31 March 2022 Issue Date: 31 March 2022

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	CAO Zilong
	HUANG Duruo

Cite this article:

CAO Zilong,HUANG Duruo. Time-frequency characteristic analyses of measured and artificial seismic waves using the XGBoost algorithm[J]. Journal of Tsinghua University(Science and Technology), 2022, 62(8): 1330-1340.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2022.25.036 OR http://jst.tsinghuajournals.com/EN/Y2022/V62/I8/1330

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