Seismic data augmentation for automatic fault picking using deep learning
Nam Pham, Sergey Fomel
Abstract
Abstract We propose a method to generate seismic images with corresponding fault labels for augmenting training data in automatic fault detection. Our method is based on two generative adversarial networks: one for creating a fault system and the other for generating two‐dimensional seismic images with faults as a condition. Our method can capture the characteristics of field seismic data during inference to generate samples that have properties of both field seismic data and synthetic training data. We then use the newly generated seismic images with corresponding fault labels to train a convolutional neural network for fault picking. We test the proposed approach on a three‐dimensional field dataset from the Gulf of Mexico. We use different areas in the field dataset as input to generate new training data for corresponding fault‐picking models. The results show that the generated training data from our method help in improving the fault‐picking models in the targeted areas.