| Paper ID | SPTM-20.5 |
| Paper Title |
A structure-guided and sparse-representation-based 3D seismic inversion method |
| Authors |
Bin She, Yaojun Wang, Guangmin Hu, University of Electronic Science and Technology of China, China |
| Session | SPTM-20: Signal Processing over Graphs and Sparsity-Aware Signal Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SSP] Statistical Signal Processing |
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| Virtual Presentation |
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| Abstract |
Existing seismic inversion methods are usually 1D, mainly focusing on improving the vertical resolution of inversion results. A few 2D or 3D inversion techniques are either too simple and lack the consideration of stratigraphic structures, or are too complicated which need to extract dip information and solve a complex constrained optimization problem. In this work, with the help of gradient structure tensor (GST) and dictionary learning and sparse representation (DLSR) technologies, we propose a 3D inversion approach (GST-DLSR) that considers both vertical and horizontal structural constraints. In the vertical direction, we investigate the vertical structural features of subsurface models from well-log data by DLSR. In the horizontal direction, we obtain the stratigraphic structural features from a 3D seismic image by GST. We then apply the acquired structural features to constraint the entire inversion procedure. The experiments show that GST-DLSR takes good advantages of both techniques, enabling to produce inversion results with high resolution, good lateral continuity, and enhanced structural features. |
