Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons
This addresses saliency detection for seismic applications, offering an incremental improvement by adapting visual attention models to directional data.
The paper tackles saliency detection in seismic data by proposing a method using 3D-FFT local spectra and multi-dimensional projections with directional comparisons, showing it outperforms state-of-the-art methods on a real dataset from the F3 block in the North Sea.
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.