A fault detection method of seismic data based on MultiResU-Net
TANG Jie1, MENG Tao1, HAN Shengyuan1, and CHEN Xueguo2
1. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong, 266580, China; 2. Exploration and Development Research Institute, Sinopec Shengli Oilfied Company, Dongying Shangdong, 257105, China
Abstract：Fault detection is significant for seismic data interpretation. Conventional fault detection methods based on coherent volume and curvature are not intuitive enough. Using manual operation, it is impossible to deal with big seismic data in actual production. Deep learning is widely used in seismic interpretation in recent years because of its powerful ability of feature extraction and expression. This paper proposes a fault detection method based on Multiresolution U-net. It can enhance the multi-scale fault detection ability on network models by introducing multi-resolution blocks, and reduce the semantic difference between concatenate feature maps by using a residual path instead of an ordinary skip connection. The trained network model has higher accuracy than the conventional U-net. The Jacard index and the Dice coefficient were increased by 0.027 and 0.136 respectively, and the fault detection error rate was reduced by 0.094.Through visual analysis of the interlayer in the network, the feature extraction and expression process was displayed intuitively. When the network is extended to 3D, and combined with transfer learning, satisfactory fault detection in raw 3D seismic data can be obtained. It is of great significance to realizing efficient and automatic fault detection in actual production work.
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TANG Jie, MENG Tao, HAN Shengyuan, and CHEN Xueguo. A fault detection method of seismic data based on MultiResU-Net. Oil Geophysical Prospecting, 2021, 56(3): 436-445.
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