Salt Detection Using Segmentation of Seismic Image

TL;DR

This paper presents a deep convolutional neural network approach for automatic segmentation of seismic images to accurately detect salt deposits beneath the earth's surface, aiming to improve over subjective human interpretation.

Contribution

The study applies a state-of-the-art DCNN to seismic image segmentation for salt detection, demonstrating promising results in automating a traditionally subjective process.

Findings

DCNN achieved promising salt detection accuracy

Automated segmentation reduces reliance on human interpretation

Method improves objectivity in seismic imaging

Abstract

In this project, a state-of-the-art deep convolution neural network (DCNN) is presented to segment seismic images for salt detection below the earth's surface. Detection of salt location is very important for starting mining. Hence, a seismic image is used to detect the exact salt location under the earth's surface. However, precisely detecting the exact location of salt deposits is difficult. Therefore, professional seismic imaging still requires expert human interpretation of salt bodies. This leads to very subjective, highly variable renderings. Hence, to create the most accurate seismic images and 3D renderings, we need a robust algorithm that automatically and accurately identifies if a surface target is a salt or not. Since the performance of DCNN is well-known and well-established for object recognition in images, DCNN is a very good choice for this particular problem and being successfully applied to a dataset of seismic images in which each pixel is labeled as salt or not. The result of this algorithm is promising.