New fault attribute based on robust directional scheme

Abstract

We present a steered data-analysis approach to measure coherence for fault detection. In contrast with conventional coherence, which detects discontinuities without distinction, our approach aims to identify faults only. Assuming the local linearity of fault geometry, the method performs a continuity test using a steered data-analysis window over a set of dip/azimuth directions. A robust, selective directional continuity test is achieved by combining measures of coherence computed from a few aligned, steered windows. Finally, fault detection consists of finding the maximum directional response and accumulating it into an attribute volume. A comparison between results obtained on both synthetic and real seismic images indicates the new method is superior to the conventional coherence measure in isolating faults from stratigraphic signature and noise. Undesired scale, staircase, and mislocation effects are reduced noticeably.