Automatic arrival-time picking of P- and S-waves of micro-seismic events based on relative standard generative adversarial network and GHRA

Abstract

Abstract Rapid, high-precision pickup of microseismic P- and S-waves is an important basis for microseismic monitoring and early warning. However, it is difficult to provide fast and highly accurate pickup of micro-seismic P- and S-waves arrival-time. To address this, the study proposes a lightweight and high-precision micro-seismic P- and S-waves arrival times picking model, lightweight adversarial U-shaped network (LAU-Net), based on the framework of the generative adversarial network, and successfully deployed in low-power devices. The pickup network constructs a lightweight feature extraction layer (GHRA) that focuses on extracting pertinent feature information, reducing model complexity and computation, and speeding up pickup. We propose a new adversarial learning strategy called application-aware loss function. By introducing the distribution difference between the predicted results and the artificial labels during the training process, we improve the training stability and further improve the pickup accuracy while ensuring the pickup speed. Finally, 8986 and 473 sets of micro-seismic events are used as training and testing sets to train and test the LAU-Net model, and compared with the STA/LTA algorithm, CNNDET+CGANet algorithm, and UNet++ algorithm, the speed of each pickup is faster than that of the other algorithms by 11.59ms, 15.19ms, and 7.79ms, respectively. The accuracy of the P-wave pickup is improved by 0.221, 0.01, and 0.029, respectively, and the S-wave pickup accuracy is improved by 0.233, 0.135, and 0.102, respectively. It is further applied in the actual project of the Shengli oilfield in Sichuan. The LAU-Net model can meet the needs of practical micro-seismic monitoring and early warning and provides a new way of thinking for accurate and fast on-time picking of micro-seismic P- and S-waves.

Keywords

• Arrival-time picking of P- and S-waves
• Generative adversarial network
• Lightweight feature extraction (GHRA)
• Application-aware loss function