Abstract
Most existing seismic data frequency enhancement methods have limitations. Given the advantages and disadvantages of these methods, this study attempts to apply deep learning technology to improve seismic data resolution. First, on the basis of the UNet deep learning method, which combines well-logging and seismic data, a synthetic seismic record is established with logging acoustic data and density, the borehole synthetic seismic record is labeled, and the borehole seismic trace data are taken as the input data. The training model of the borehole seismic trace data and the borehole synthetic seismic record is established to improve the medium- and high-frequency information in the seismic data. Second, the gate recurrent unit (GRU) is used to retain the low-frequency trend in the original seismic record, and the UNet and GRU results are combined to improve the medium- and high-frequency information while preserving the low-frequency information in the seismic data. Then, model training is performed, the model is applied to the three-dimensional seismic data volume for calculation, and the seismic data resolution is improved. The information extracted using our method is more abundant than that extracted using previous methods. The application of a theoretical model and actual field data shows that our method is effective in improving the resolution of poststack seismic data.
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Acknowledgments
This project is supported by the Open Fund project of Jiangxi Research Center of Nuclear Geoscience Data Science and Systems Engineering Technology “Research on intelligent recognition method of low-order fault based on V-net deep learning architecture” (JETRCNGDSS202205), “Study on the method of identifying the superior reservoir of tight sandstone based on depth learning” (JETRCNGDSS202103), School-level project of the East China University of Technology “Study on the method of identifying low-order faults with geological big data” (DHBK2019222), and the Ministry of Education 2021 the first batch of industry–university collaboration projects (202101185011).
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Guo Ai-Hua, M.S., graduated from China University of Geosciences Beijing in 2006. She works at the East China University of Technology. Her main research interests are seismic signal processing and interpretation.
Lu Peng-Fei, Ph.D., graduated from the Institute of Geology and Geophysics, Chinese Academy of Sciences, in 2008. His main research interest is artificial intelligence seismic signal processing.
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Guo, AH., Lu, PF., Wang, DD. et al. Improving the resolution of poststack seismic data based on UNet+GRU deep learning method. Appl. Geophys. 20, 176–185 (2023). https://doi.org/10.1007/s11770-023-1038-7
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DOI: https://doi.org/10.1007/s11770-023-1038-7