We design a deep learning workflow to interactively track seismic geobodies, such as salt bodies and faults. The algorithm is based on a flood-filling network, which performs iterative segmentation and moving the field of view (FoV). Instead of an end-to-end segmentation from the image to the classification mask, the proposed network takes the previous mask output, together with the seismic image in a new FoV, as a combined input to predict the mask at this FoV. The movement of the FoV is guided by the flood-filling algorithm in order to visit and segment the full extent of a geobody. Unlike the conventional seismic image segmentation methods that can only output attribute volumes, the proposed workflow can not only detect geobodies but also track individual geobody instances.
Presentation Date: Tuesday, September 17, 2019
Session Start Time: 8:30 AM
Presentation Time: 9:20 AM
Location: 221C
Presentation Type: Oral
