  In this paper, we propose a novel approach for saliency detection for seismic
applications using 3D-FFT local spectra and multi-dimensional plane
projections. We develop a projection scheme by dividing a 3D-FFT local spectrum
of a data volume into three distinct components, each depicting changes along a
different dimension of the data. The saliency detection results obtained using
each projected component are then combined to yield a saliency map. To
accommodate the directional nature of seismic data, in this work, we modify the
center-surround model, proven to be biologically plausible for visual
attention, to incorporate directional comparisons around each voxel in a 3D
volume. Experimental results on real seismic dataset from the F3 block in
Netherlands offshore in the North Sea prove that the proposed algorithm is
effective, efficient, and scalable. Furthermore, a subjective comparison of the
results shows that it outperforms the state-of-the-art methods for saliency
detection.
