Fault Feature Enhancement in Seismic Data Based on Steerable Pyramid Tensor Voting

Although faulting is usually discontinuous for some geologic environments, many faults or parts of the faults are weak discontinuities or even invisible in seismic images due to seismic resolution and noises. Traditional fault detection methods often lead to blurred, low-integrity fault attributes. These blurred attributes may hinder insights into geologic interpretation. We develop a multiscale and multidirectional fault enhancement method called steerable pyramid tensor voting (SPTV) to overcome this difficulty. The proposed method consists of two cascaded steps. The steerable pyramid step generates multiscale and multidirectional seismic attributes. These attributes at different scales are enhanced by optimal directional filtering and then reconstructed to improve the fault resolution. The tensor voting step discovers the hidden fault features by voting from the adjacent faults. This step enhances the fault integrity and linearity and is able to extract the fault skeletons. We test our method using a benchmark model and confirm its effectiveness in identifying faults. We apply this method to identify the faults in a tight sand reservoir of the Sichuan Basin, China. The results show that the method can effectively enhance the fault and subfault features and enable the clear identification of faults compared with traditional methods. Furthermore, the results exhibit a reasonable congruency with the geologic and well-production data.