A Structure-Guided and Sparse-Representation-Based 3d Seismic Inversion Method

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.