A Graph Neural Network Based Approach for Fault Delineation in Seismic Data using Graph Total Variation and Multigraph

Interpreting seismic data involves finding out subsurface geologic information. In seismic data interpretation, one of the crucial steps is to delineate seismic faults. Natural gas and oil reservoirs are more likely to be present where seismic faults exist. In this paper, we develop a graph neural network based approach for finding faults in seismic data using graph total variation. Our proposed methodology begins with the first step, the extraction of patches for all training points (pixels). In the graph domain, these patches appear as individual graphs. We use the graph total variation as graph attributes and seismic amplitudes as node attributes for the graphs. The next step is implementing graph neural networks (GNNs) for graph classification and fault delineation. The proposed methodology offers higher accuracy and improved time complexity when implemented on real data.