Abstract：Surface microseismic signals are greatly affected by noises and have a low signal-to-noise ratio,which has a serious impact on the subsequent work of microseismic first arrival picking and imaging positioning. Therefore, the denoising of microseismic signals is a key step in the preprocessing of microseismic data. Conventional denoising methods often depend on the settings of algorithm parameters,and thus do not have universal applicability. This paper proposes a denoising method for microseismic signals based on the bidirectional long short-term memory (Bi-LSTM) neural network. First,we use synthetic signal and actual signal to construct the sample data set. By training and testing the constructed Bi-LSTM model,we obtain the model with the best denoising effect. Then,the trained network is used to denoise the synthetic signals with different signal-to-noise ratios and the microseismic signals from the actual fracturing monitor in the Sichuan-Chongqing area. The denoised actual microseismic signals are utilized for seismic emission tomography (SET), and the source location of surface microseismic is realized through analyzing the SET images. The experimental results show that the proposed method can effectively reduce various noises in microseismic signals and improve the signal-to-noise ratio,so as to improve the accuracy of source location. Compared with the traditional algorithm,the method does not depend on the adjustment of some parameters in the algorithm and has good generalization characteristics.
李佳, 王维波, 盛立, 高明. 应用双向长短时记忆神经网络的微地震信号降噪方法[J]. 石油地球物理勘探, 2023, 58(2): 285-294.
LI Jia, WANG Weibo, SHENG Li, GAO Ming. Denoising of microseismic signal based on bidirectional long short-term memory neural network. Oil Geophysical Prospecting, 2023, 58(2): 285-294.
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