A Deep Supervised Edge Optimization Algorithm for Salt Body Segmentation

Abstract

Seismic image analysis plays a vital role in a wide range of industrial applications and has received widespread attention. One of the main challenges of seismic image analysis is detecting underground salt structures, which is essential for identifying oil and gas reservoirs and planning drilling paths. Currently traditional seismic image analysis still requires professionals to analyze the salt body. Convolutional neural networks have been successfully applied in many fields, and several attempts have been made in the field of seismic imaging. In this letter, we propose a deep-supervised method which effectively segments the salt body. We design an edge prediction branch to predict the boundary of the salt body, which guides feature learning through the supervision of boundary loss, so that the network can make the features on both sides of the semantic boundary distinguishable. We show that our approach outperforms state-of-the-art methods on the TGS Salt Identification Challenge data set and experimental results demonstrate the effectiveness of the proposed method. The source code is available at GitHub.The source code is available at. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> The source code is available at <uri>https://github.com/Gjiangtao/A-Deep-Supervised-Edge-Optimization-Algorithm-for-Salt-Body-Segmentation</uri>. </fn>