Many seismic attributes today reveal fault and fracture patterns. However, these fault attributes often suffer from noise or artifacts in the input data, resulting in data that is not high enough a quality for automatic fault pickers to use directly without a post-attribute enhancement. In a paper written for and presented in Dallas, Texas at the SEG International Exposition and 86th Annual Meeting on October 16-21, we introduced a new method to enhance the fault patterns in a 3D seismic volume. This method uses an array of 3D log-Gabor filters, which optimize fault planes by identifying isolated sections, as a coherent fault or fracture, while suppressing footprints, noise, and other artifacts.
The resulting fault energy volume, which represents the enhanced faults, assists with fault interpretation. Unlike conventional fault enhancement, our method is inspired by the neuronal mechanism of orientation perception in the brain, and does not require fault orientations as input for filtering. Instead, fault dip and azimuth are two additional output attributes which are estimated during the filtering process, and are used for further orientation analysis or volumetric fault visualization. The proposed method is applied to real-world 3D seismic data located at Great South Basin, New Zealand offshore, which contains complex fault networks.