# The role of visual saliency in the automation of seismic interpretation

## TL;DR

This paper introduces SalSi, a seismic attribute inspired by human visual perception, to improve the detection and delineation of salt domes in seismic data, demonstrating high accuracy and efficiency.

## Contribution

The paper presents a novel workflow using SalSi for seismic structure delineation, combining saliency detection with region growing for improved salt dome boundary detection.

## Key findings

- High precision and accuracy in salt-dome boundary detection
- Outperforms existing delineation algorithms in speed and quality
- Effective on real seismic data from North Sea, F3 block

## Abstract

In this paper, we propose a workflow based on SalSi for the detection and delineation of geological structures such as salt domes. SalSi is a seismic attribute designed based on the modeling of human visual system that detects the salient features and captures the spatial correlation within seismic volumes for delineating seismic structures. Using SalSi, we can not only highlight the neighboring regions of salt domes to assist a seismic interpreter but also delineate such structures using a region growing method and post-processing. The proposed delineation workflow detects the salt-dome boundary with very good precision and accuracy. Experimental results show the effectiveness of the proposed workflow on a real seismic dataset acquired from the North Sea, F3 block. For the subjective evaluation of the results of different salt-dome delineation algorithms, we have used a reference salt-dome boundary interpreted by a geophysicist. For the objective evaluation of results, we have used five different metrics based on pixels, shape, and curvedness to establish the effectiveness of the proposed workflow. The proposed workflow is not only fast but also yields better results as compared to other salt-dome delineation algorithms and shows a promising potential in seismic interpretation.