Diffraction wave separation and imaging with deep learning network based on Encoder-Decoder framework
MA Ming1,2, BAO Qianzong1,2
1. School of Geological Engineering and Geomatics, Chang'an University, Xi'an, Shaanxi 710000, China; 2. National Engineering Research Center of Offshore Oil and Gas Exploration, Beijing 100028, China
Abstract：Identification of underground geological abnormal bodies only by diffracted wave fields has a solid theoretical foundation, and corresponding implementation methods have been widely studied and effectively applied in actual exploration. However, the existing techniques have made slight progress in micro-scale abnormal body ima-ging, and the most of related studies are based on the theory of ray propagation, which have an incomplete analysis of factors affecting the accuracy of separated imaging. Compared with reflected waves, diffracted waves ge-nerated by discontinuous structures have weak energy and usually interfere with each other, and environmental interferences further eliminate the diffracted waves. Therefore, high-precision wave field separation and indepen-dent imaging are the key research directions of current interpretation processing based on the ultra-high resolution of diffracted waves. First, according to the accurate positioning of micro-scale target bodies in geophysical prospecting, this paper takes the diffracted waves with high-resolution information as the research object and systema-tically analyzes the energy magnitude and morphological characteristics of the diffracted waves under abnormal bodies with different scales. The goal of this process is to master the specific superposition forms of diffracted waves and other types of waves. Second, from the corresponding characteristic properties, the paper proposes a separate method of diffracted waves based on deep learning technology. New method introduces the Encoder-Decoder framework and atrous convolution to capture the properties of diffraction wave. In virtue of the outputted unique diffraced wave field, we could calculate the migration velocity via continuing velocity criterion. Finally, migrated data and velocity model are obtained. Data tests show that the proposed method can realize high-precision identification of micro-scale geological anomaly bodies in industrial production.
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MA Ming, BAO Qianzong. Diffraction wave separation and imaging with deep learning network based on Encoder-Decoder framework. Oil Geophysical Prospecting, 2023, 58(1): 56-64.
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