Comparing 2D and 3D Convolutional Neural Networks to Predict Fault Surfaces for Structural Model Building

Summary This abstract shows the capability of convolutional neural networks (CNN) to predict faults on a challenging onshore Texas dataset. Furthermore, the paper compares the quality of the prediction cubes generated from pretrained 2D and 3D convolutional neural networks (CNN) to extract fault surfaces suitable for structural model building construction.