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Hydraulic fracturing microseismic first arrival picking method based on non-subsampled shearlet transform and higher-order-statistics

GUANQUN SHENG1,2 ,  XINGONG TANG3,1 ,  KAI XIE2 ,  JIE XIONG2
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Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Wuhan 430100, P.R. China.,
National Demonstration Center for Experimental Electrical and Electronic Education, Yangtze University, Jingzhou, Hubei 434000, P.R. China. Electronics and Information School, Yangtze University, Jingzhou, Hubei 434000, P.R. China.,
College of Geophysics and Petroleum Resources, Wuhan 430100, P.R. China,
JSE 2019, 28(6), 593–618;
Submitted: 9 June 2025 | Accepted: 9 June 2025 | Published: 1 December 2019
© 2019 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Sheng, G.Q., Tang, X.G., Xie, K. and Xiong, J., 2019. Hydraulic fracturing microseismic first arrival picking method based on non-subsampled shearlet transform and higher-order-statistics. Journal of Seismic Exploration, 28: 593-618. Fast and accurate first arrival picking is the key issue of microseismic data processing. Traditionally manual picking methods will take a lot of time and reduce the data processing efficiency, so it is difficult to meet the demand of real-time data processing for microseismic monitoring. In this paper, we proposed the S-S/L_K (Shearlet-Short time window/Long time window-Kurtosis) algorithm which combined the shearlet multiscale decomposition with higher-order-statistics (HOS). This algorithm not only keeps the advantage of non-subsampled shearlet transform in multiscale analysis, but also maintain strengths of HOS in signal abnormalities detection and Gaussian noise suppressing. The forward records and real data tests show that compared with the PAI-S/K and the STA/LTA algorithm, the proposed method can overcome the influence of noise on the P-phase picking accuracy and obtain a reliable P-phase result for microseismic monitoring.

Keywords
microseismic monitoring
non-subsampled shearlet transform
higher-order-statistics
first arrival picking
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