Fault Detection Using Attention Models Based on Visual Saliency

In this paper, we present an approach for detecting faults within seismic volumes using a saliency detection framework that employs a 3D-FFT local spectra and multi-dimensional plane projections. The projection scheme divides a 3D-FFT local spectrum into three distinct components, each depicting variations along different dimensions of the data. To detect seismic structures oriented at different angles and to capture directional features within 3D volume, we modify the center-surround model to incorporate directional comparisons around each voxel. The weighted combination of the obtained features then yields a saliency map. Experimental results on a real seismic dataset from the Great South Basin in New Zealand show the effectiveness of the proposed algorithm in the detection of complex fault networks, which are hardly conspicuous within original seismic volume. The subjective evaluation of the results show that the proposed method outperforms the state-of-the-art saliency algorithms and seismic attributes in detecting complex structures and holds a promising future in computer-aided extraction of other geologic features as well.