Combining Multiple Seismic Attributes using Convolutional Neural Networks

Abstract

Seismic exploration involves estimating the properties of the Earth's subsurface from reflected seismic waves then visualizing the resulting seismic data and its attributes. These data and derived seismic attributes provide complementary information and reduce the amount of time and effort for the geoscientist. Multiple conventional methods to combine various seismic attributes exist, but the number of attributes is always limited, and the quality of the resulting image varies. This paper proposes a method that can be used to overcome these limitations. In this paper, we propose using Deep Learning-based image fusion models to combine seismic attributes. By using convolutional neural network (CNN) capabilities in feature extraction, the resulting image quality is better than that obtained with conventional methods. This work implemented two models and conducted a number of experiments using them. Several techniques have been used to evaluate the results, such as visual inspection, and using image fusion metrics. The experiments show that the Image-fusion Framework, using the Image Fusion Framework Based on CNN (IFCNN) approach, outperformed all other models in both quantitative and visual analysis. Its QAB/F and MS-SSIM scores are 50% and 10%, respectively, higher than all other models. Also, IFCNN was evaluated against the current state-of-the-art solution, Octree, in a comparative study. IFCNN overcomes the limitation of the Octree method and succeeds in combining nine seismic attributes with a better-combining quality, with QAB/F and NAB/F scores being 40% higher.