Seismic P-Wave First-Arrival Picking Model Based on Spatiotemporal Attention Mechanism

Aiming at the problems of low accuracy and poor robustness of the existing earthquake first-arrival picking algorithm, a seismic P-wave arrival picking network based on deep learning is designed. This network is encoder-decoder structure, which can identify seismic signal sequence point by point. The encoder uses multi-scale feature extractor for feature extraction and fusion of input data to improve feature utilization ratio. The multi-scale residual structure is used to deeply mine the hidden feature information in the data to improve the nonlinear fitting ability of the model. Then, the spatiotemporal attention mechanism is added to the decoder to improve the network’s perception of the first-arrival features. Finally, a deep coding feature fusion module is proposed to effectively avoid the pollution of feature sequence while ensuring the integrity of features. The experimental results show that under the three error thresholds of 0.1 s, 0.2 s and 0.3 s, the picking hit rate of the proposed network are 75.04%, 94.6% and 97.37%, respectively, the mean absolute error and mean square error are 0.092 s and 0.036. Compared with the existing traditional and deep learning first-arrival picking methods, it has higher P-wave first-arrival picking accuracy. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.