Automatic seismic fault detection and surface construction

This paper proposes an effective approach for automatically building the fault model based on the 3D seismic images via two steps of automatic seismic fault detection and fault surface construction. Automatic seismic fault detection is performed to automatically classify the seismic image into two phases of fault and background using a slightly revised deeplabv3_resnet50 architecture with pretrained parameters provided by PyTorch. The output of the automatic seismic fault detection is a binary image contains fault and background, where one fault may be separated into different fault segments, or several faults are connected with each other which need further distinguish. To reassemble these detected fault segments and construct the fault surface model, four steps are implemented including :1) a morphological workflow is used to separate all connected faults into separated fault segments; 2) the moving least square (MLS) method is used to fit each fault segments as a smooth, one-voxel thickness surface; 3) the weighted principle component analysis (WPCA) method is applied to calculate the normal vector of each surface voxel to judge whether two or more adjacent segments should be combined in one fault surface; 4) MLS method is applied again to fit all surface segments from one fault as an unique fault surface. The final output of the proposed method provides a fault model with well-defined, cleanly separated, labeled fault surfaces that is competent for structure modelling.