Automatic Salt Segmentation Using Deep Learning Techniques

Abstract

Detecting salt boundaries in seismic images is critical for subsurface reservoir characterization and oil and gas exploration. The presence of salt in seismic data often indicates the presence of valuable hydrocarbon resources, which could lead to significant oil discoveries. Traditional manual interpretation methods have limitations, prompting the industry to embrace deep learning techniques. Our proposed system extensively evaluates the effectiveness of deep learning models in salt boundary detection. Our approach involves developing a custom residual encoder-decoder model and comparing it against two existing models: Res-UNet and UNet. The advantage of our custom-built residual encoder-decoder model lies in its utilization of transposed convolutions for image segmentation. Unlike regular convolutions that extract features and reduce image size, transposed convolutions expand the image, potentially introducing new information from seismic data. The custom model emerges superior to UNet and Res-UNet models, exhibiting an accuracy of 94.28% and a precision score of 0.86. A series of comparative analysis is drawn with a main focus on transforming the automated salt segmentation process.