A fault-detection workflow using deep learning and image processing

Abstract

Within the last a couple of years, deep learning techniques, represented by convolutional neural networks (CNNs), have been applied to fault detection problems on seismic data with impressive outcome. As is true for all supervised learning techniques, the performance of a CNN fault detector highly depends on the training data, and post-classification regularization may greatly improve the result. Sometimes, a pure CNN-based fault detector that works perfectly on synthetic data may not perform well on field data. In this study, we investigate a fault detection workflow using both CNN and directional smoothing/sharpening. Applying both on a realistic synthetic fault model based on the SEAM (SEG Advanced Modeling) model and also field data from the Great South Basin, offshore New Zealand, we demonstrate that the proposed fault detection workflow can perform well on challenging synthetic and field data. Presentation Date: Tuesday, October 16, 2018 Start Time: 8:30:00 AM Location: 204B (Anaheim Convention Center) Presentation Type: Oral