A computational topology-based method for extracting fault surfaces

Abstract

Fault surface extraction is a crucial step in seismic interpretation, which can help structural interpretation and structural modeling. A key focus of fault surface extraction research is to extract fault surfaces in their entirety as much as possible, rather than just in fault segments, which is more challenging in some complex fault situations. To address this challenge, we develop a fault surface extraction method based on computational topology to extract fault surfaces in their entirety as much as possible from a fault attribute and effectively handle some complex fault situations, such as intersecting faults. From a given seed point on the target fault, we use the idea of regional growth to search for high-confidence points on the target fault, called fault control points, under the constraints of the fault attribute and the calculated fault orientations. Through these fault control points, we extract the fault boundary and process the fault attribute so that only the target fault is included. Furthermore, we use an operation in computational topology called collapse to extract the target fault from the processed fault attribute using the fault boundary as a constraint. By incorporating fault orientation information and using a relatively large search distance during the control point search, our method enables the integration of segmented faults and facilitates the handling of complex fault situations such as intersecting faults. The collapse operation ensures that the extracted fault surfaces align with the fault attribute, correspond to the actual fault locations in seismic data, and enhance fault continuity. In addition, we develop an automatic method for picking seed points to realize the extraction of all the faults in the research data. We test our method on several field data sets and the experimental results demonstrate its effectiveness. In some complex fault situations, such as intersecting faults, our method performs well and indicates a significant improvement over the compared method.