Fault surface construction method based on point cloud surface reconstruction

Fault surface construction plays an important role in seismic structural interpretation and building structural models. Significant research studies have been carried out regarding fault surface extraction in the past few years. In these studies, the main challenges include the following: some fault samples are locally missing, the noise and the geological discontinuity features, some fault surfaces may form complicated intersections with each other, and some adjacent faults with similar directions are difficult to classify. In this paper, we propose a point cloud surface reconstruction method to automatically construct and classify the fault surfaces from a 3D seismic image so that the fault surfaces are constructed completely and accurately. In this method, we first use the fault-scanning method to smooth the fault attribute image and thin the smoothed fault attribute image. We then pick the seed points from the thinned attribute image as control points and use the random sample consensus method to compute the optimal surface that passes through these seed points. Finally, we construct the complete fault surfaces by merging all these optimal surface patches and use moving least square (MLS) to reconstruct the fault surfaces to smooth them and interpolate possible holes. With fitting the fault surfaces by MLS, we can also accurately estimate fault orientations. We demonstrate the efficiency and effectiveness of the method by using model seismic data and open access seismic data that are complicated by intersecting faults and noise.