Volumetric seismic dip and azimuth estimation with 2D log-gabor filter array

Abstract

In this paper, we propose a novel method of orientation estimation in seismic data. Unlike conventional methods of orientation estimation with a context window, our method is inspired by the neural mechanism of visual perception. The primary visual cortex in the brain contains orientation columns, which are composed of an array of orientation detectors. A log-Gabor filter with a specific orientation and scale configuration simulates the neuronal mechanism of the orientation detector, while an array of such filters simulates the orientation columns. The resulting orientation is derived at the sample level of the input seismic data: the filter response competes with each other in the array, and the maximum response defines the seismic orientation. Our proposed method has many applications in seismic interpretation, e.g., calculating the volumetric azimuth and dip attributes without picking, guiding seismic attribute computation, and detecting seismic texture patterns.