Research on depth residual method of super-resolution for intelligent seismic wave first arrival pickup
LI Jianping1,2, ZHANG Shuowei1, DING Renwei1,3, MA Xiaomin1, ZHAO Lihong1,3, ZHAO Shuo1
1. College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China; 2. Shandong Institute of Geophysical & Geochemical Exploration, Jinan, Shandong 221116, China; 3. Qingdao National Laboratory for Marine Science and Technology, Functional Laboratory for Marine Mineral Resources Evaluation and Exploration Technology, Qingdao, Shandong 266237, China
Abstract：In view of the low precision and poor generalization ability in the first arrival pickup of conventional semantic segmentation networks, an intelligent first arrival pickup method for depth residual network of super-reso-lution (SD-Net) based on a U-Net network, residual learning module, and subpixel convolution method is proposed. This method uses a U-shaped network with a jump connection to fuse multi-scale information of seismic data and simplifies the work through end-to-end training. Firstly, the residual learning module is introduced in the downsampling stage of SD-Net to overcome the deep network degradation problem and effectively improve the learning ability of seismic data. Secondly, the subpixel convolution method is used in the upsampling stage to achieve the super-resolution reconstruction of the feature map through convolution and multi-channel pixel recombination, and the positioning of the first arrival is achieved with higher accuracy. In addition, transfer learning is utilized to apply the model to the simulated data with a medium and low signal-to-noise ratio (SNR), and the optimal first arrival pickup model can be obtained by training only a small amount of labeled data. Practical examples show that the training efficiency of the SD-Net is significantly improved compared with that of the U-Net method. The network model has higher accuracy and robustness. The results predicted by the transfer learning model prove that SD-Net has a strong generalization ability. This method is of great significance for realizing efficient and accurate intelligent first arrival pickup in actual production.
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