Robust Seismic Volumetric Dip Estimation Combining Structure Tensor and Multiwindow Technology

Abstract

As one type of important seismic geometric attributes, the seismic volumetric dip is extensively used to assist interpretation of horizons, faults, and other geologic structures in 3-D seismic data. In this paper, we mainly focus on estimating seismic volumetric dip robustly and try to reduce the influences of amplitude's lateral changes, faults, and other discontinuous structures. We first use the instantaneous phase (IP) as one fundamental data set to reduce the influence of amplitude's lateral variation. Second, we construct structure tensor (ST) on IP and apply eigendecomposition on corresponding ST covariance matrix to obtain three eigenvalues and corresponding eigenvectors. Then, the seismic volumetric dip can be calculated from the dominant eigenvector, and a similarity measure can be constructed based on these three eigenvalues. Third, based on the similarity measure, we reduce the influence of fault on dip estimation by using multiwindow technology if the analyzing window spans a fault. Finally, we applied our method to three synthetic data examples and two field data examples. The results of seismic volumetric dip and curvature estimation verify that the proposed method has better antinoise and antifault performance comparing with the corresponding sophisticated method in commercial software and the conventional ST-based method.