PSSegNet: Segmenting the P- and S-Phases in Microseismic Signals through Deep Learning

Zhengxiang He, Xingliang Xu (), Dijun Rao, Pingan Peng, Jiaheng Wang and Suchuan Tian
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Zhengxiang He: State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, China
Xingliang Xu: State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, China
Dijun Rao: Zijin Mining Group Co., Ltd., Longyan 364200, China
Pingan Peng: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Jiaheng Wang: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Suchuan Tian: State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, China

Mathematics, 2023, vol. 12, issue 1, 1-19

Abstract: Microseismic P- and S-phase segmentation is an influential step that limits the accuracy of event location, parameter inversion, and mechanism analysis. Therefore, an improved Unet named PSSegNet is proposed to intelligently segment the P- and S-phases. The designed masks are used as the outputs of PSSegNet, which is used to obtain the time–frequency features of the P- and S-phases. As a result, the MSE (mean square error) between the predicted mask and the actual labeled mask is concentrated below 2.5, and the AE (accumulated error) of the reconstructed P/S-phase based on the predicted mask is concentrated below 1.0 × 10 −3 . Arrival picking results show that the overall error of the entire test set is less than 50 ms and most of the errors are less than 20 ms. Data with SNR (signal to noise ratio) < 2, 2 ≤ SNR < 3, PSR (P-phase to S-phase ratio) < 1, or 1 ≤ PSR < 2 in the dataset were selected for arrival picking and their errors were counted. The statistical results show that PSSegNet is robust at low SNR and PSR. The P- and S-phase segmentation based on PSSegNet has excellent potential for use in various applications and can effectively reduce the difficulty of obtaining the P/S-phase arrivals.

Keywords: microseismic; deep learning; segmentation; P- and S-phase; signal processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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