Automatic Classification of Geologic Units in Seismic Images Using Partially Interpreted Examples
B. Peters, J. Granek and E. Haber
Event name: 81st EAGE Conference and Exhibition 2019
Session: AI/Digitalization for Interpretation - Geological and Model Building Interpretation
Publication date: 03 June 2019
Info: Extended abstract, PDF ( 1.16Mb )
Price: € 20
Geologic interpretation of large seismic stacked or migrated seismic images can be a time-consuming task for seismic interpreters. Neural network based semantic segmentation provides fast and automatic interpretations, provided a sufficient number of example interpretations are available. Networks that map from image-to-image emerged recently as powerful tools for automatic segmentation, but standard implementations require fully interpreted examples. Generating training labels for large images manually is time consuming. We introduce a partial loss-function and labeling strategies such that networks can learn from partially interpreted seismic images. This strategy requires only a small number of annotated pixels per seismic image. Tests on seismic images and interpretation information from the Sea of Ireland show that we obtain high-quality predicted interpretations from a small number of large seismic images. The combination of a partial-loss function, a multi-resolution network that explicitly takes small and large-scale geological features into account, and new labeling strategies make neural networks a more practical tool for automatic seismic interpretation.