Sparse generalized S-Transform and its application to detection of low-frequency seismic anomalies in reservoirs
LIU Junjie1, CHENG Xuehua1,2, WU Haojie2, ZHANG Jie2, JIANG Xiaomin2
1. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, Sichuan 610059, China; 2. Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu, Sichuan 610059, China
Abstract：Time-frequency analysis is an important method in seismic data processing and interpretation. Time-frequency resolution is the key to high-precision reservoir prediction, but the time-frequency resolution of conventional and generalized S-Transform cannot meet the needs of high-precision reservoir prediction. Therefore, this paper introduces the idea of sparse constraint into the time-frequency analysis, and constructs a sparse genera-lized S-Transform method by optimizing the window matrix based on flexibly adjusted generalized S-Transform parameters. The comparative analysis of synthesized signals shows that the sparse generalized S-Transform method can obtain the time-frequency distribution with higher time-frequency resolution and better energy aggregation, and maintain high time resolution at both high and low frequencies. In the low-frequency shadow detection of actual seismic data, the proposed method can describe the spatial distribution of oil and gas reservoirs more clearly, which is beneficial to reduce the multi-solution of oil and gas reservoir detection.
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