Seismic Fault Analysis Using Curvature Attribute and Visual Saliency

In this paper, we propose a fault extraction method using curvature attribute and log -Gabor filter with the help of saliency technique, which is the improvement of the dip, edge, and azimuth attributes. In these attributes, sometimes pieces of information are confusing so interpretation can be diverted from the aim. Different types of curvatures are calculated, but the most positive and most negative curvatures are taken into account because these are more sensitive to the edges. These curvature attributes ascertain the fault lineaments, which are easy to contain within the seismic section. The visual saliency approach is incorporated for probable fault point highlighting on the curvature attribute rather than conventional coherence techniques. Further, the log-Gabor filter is used for the enhancement of the fault edges. The proposed approach results in fault detection in the seismic volume. Experimental results validate the robustness of the algorithm.