# Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons

## TL;DR

This paper introduces a novel seismic saliency detection method using 3D-FFT spectral projections and directional comparisons, outperforming existing techniques in accuracy and efficiency on real datasets.

## Contribution

It presents a new spectral projection scheme combined with a directional center-surround model tailored for seismic data analysis.

## Key findings

- Effective saliency detection on real seismic data
- Outperforms state-of-the-art methods
- Efficient and scalable algorithm

## Abstract

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.