An Intelligent First-Arrival Picking Method of Microseismic Signals Based on the Small Sample Expansion

Abstract

An accurate and efficient first-arrival picking method of microseismic signals is essential for improving the microseismic recording processing performance. However, the weak seismic magnitude and low signal-to-noise ratio (SNR) of microseismic signals make highly accurate and efficient microseismic signal processing challenging when traditional signal picking methods are used. Therefore, to use microseismic signal features effectively and improve both the accuracy and the efficiency of the first-arrival picking of microseismic signals, this article proposes a first-arrival picking method of microseismic signals based on deep learning. In addition, a generative adversarial network with latent space and adaptive layer-instance normalization for microseismic signals generation (G-LA-MSG) is used to generate a large number of effective microseismic signals under unsupervised conditions to expand the microseismic data having a limited number of samples. On the basis of the sample expansion, an AND-OR grammar pyramid scene parsing network (AOG-PSPNet) is designed to select the first arrivals of microseismic signals. By adopting the AND-OR grammar block (AOGBlock) structure, the network can fully extract the sequence data features, achieve good SNR separation in a low-SNR environment, and improve the accuracy of first arrivals of low-SNR microseismic signals. The results of the arrival picking experiments conducted under the condition of a low SNR for synthetic microseismic records and real microseismic records show that the proposed method can achieve a superior performance compared with the commonly used deep learning-based methods for first-arrival picking of microseismic signals.