# SalSi: A new seismic attribute for salt dome detection

## TL;DR

This paper introduces SalSi, a saliency-based seismic attribute inspired by human vision, designed to improve salt dome detection in seismic data, validated through real dataset experiments and ROC analysis.

## Contribution

SalSi is a novel seismic attribute leveraging saliency theory for enhanced salt dome detection, integrating human visual attention modeling.

## Key findings

- SalSi effectively highlights salt domes in seismic volumes.
- ROC analysis confirms SalSi's high detection accuracy.
- Validated on real North Sea seismic data.

## Abstract

In this paper, we propose a saliency-based attribute, SalSi, to detect salt dome bodies within seismic volumes. SalSi is based on the saliency theory and modeling of the human vision system (HVS). In this work, we aim to highlight the parts of the seismic volume that receive highest attention from the human interpreter, and based on the salient features of a seismic image, we detect the salt domes. Experimental results show the effectiveness of SalSi on the real seismic dataset acquired from the North Sea, F3 block. Subjectively, we have used the ground truth and the output of different salt dome delineation algorithms to validate the results of SalSi. For the objective evaluation of results, we have used the receiver operating characteristics (ROC) curves and area under the curves (AUC) to demonstrate SalSi is a promising and an effective attribute for seismic interpretation.