We improve automatic structural interpretation in seismic images by using CNNs (convolutional neural networks), which recently have shown the best performance in detecting and extracting useful image features and objects. The main limitation of applying CNN in seismic interpretation is the preparation of many training datasets and especially the corresponding geologic labels. To solve this problem, we propose a workflow to automatically build diverse structure models with realistic folding and faulting features. In this workflow, with some assumptions about typical folding and faulting patterns, we simulate structural features in a 3D model by using a set of parameters. By randomly choosing the parameters from some predefined ranges, we are able to automatically generate numerous structure models with realistic and diverse structural features. Based on these structure models with known structural information, we further automatically create numerous synthetic seismic images and the corresponding ground truth of structural labels to train CNNs for structural interpretation in field seismic images. Accurate results of structural interpretation in multiple field seismic images show that the proposed workflow simulates realistic and generalized structure models from which the CNNs effectively learn to recognize real structures in field images.
Presentation Date: Monday, September 16, 2019
Session Start Time: 1:50 PM
Presentation Start Time: 2:40 PM
Location: 301B
Presentation Type: Oral
