Research on fault detection method based on 3D deeply supervised network
Wang Jing1, Zhang Junhua1, Lu Fengming2, Meng Ruigang2, Wang Zuoqian1, Chang Jianqiang1
1. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. Research Institute of Exploration & Development, Dagang Oilfield Company, PetroChina, Tianjin 300280, China
Abstract：Manual interpretation of seismic faults is often uncertain. Amid the progress in computers and artificial intelligence, deep learning technologies are increasingly used in the field of geophysics, and a variety of algorithms based on the convolutional neural network are widely applied to fault recognition. In this paper, we propose a fault detection method based on the 3D deeply supervised network by combining 3D U-Net and the deep residual network and introducing the mechanism of multi-layer deep supervision. The residual block can simplify the learning goal of the network and reduce the difficulty of training, and the multi-level deep supervision provides more feedback to the network and alleviates the potential vanishing gradient problem during training, which enables the decoder sub-network to take advantage of multi-scale information and further improve the accuracy of fault detection. The theoretical model test and seismic raw data have proved that the 3D deeply supervised network can correctly identify the fault location; compared with the conventional U-Net, it reduces missed and misidentified small faults; the details of faults are more abundant and the accuracy of fault detection can be significantly improved.
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Wang Jing, Zhang Junhua, Lu Fengming, Meng Ruigang, Wang Zuoqian, Chang Jianqiang. Research on fault detection method based on 3D deeply supervised network. Oil Geophysical Prospecting, 2021, 56(5): 947-957.
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