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A new approach to seismic interpretation is proposed to leverage visualperception and human visual system modeling. Specifically, a saliency detectionalgorithm based on a novel attention model is proposed for identifyingsubsurface structures within seismic data volumes. The algorithm employs 3D-FFTand a multi-dimensional spectral projection, which decomposes local spectrainto three distinct components, each depicting variations along differentdimensions of the data. Subsequently, a novel directional center-surroundattention model is proposed to incorporate directional comparisons around eachvoxel for saliency detection within each projected dimension. Next, theresulting saliency maps along each dimension are combined adaptively to yield aconsolidated saliency map, which highlights various structures characterized bysubtle variations and relative motion with respect to their neighboringsections. A priori information about the seismic data can be either embeddedinto the proposed attention model in the directional comparisons, orincorporated into the algorithm by specifying a template when combiningsaliency maps adaptively. Experimental results on two real seismic datasetsfrom the North Sea, Netherlands and Great South Basin, New Zealand demonstratethe effectiveness of the proposed algorithm for detecting salient seismicstructures of different natures and appearances in one shot, which differssignificantly from traditional seismic interpretation algorithms. The resultsfurther demonstrate that the proposed method outperforms comparablestate-of-the-art saliency detection algorithms for natural images and videos,which are inadequate for seismic imaging data.

A novel attention model for salient structure detection in seismic volumes