Improved automatic seismic CO2 leakage detection via dataset augmentation

TitleImproved automatic seismic CO2 leakage detection via dataset augmentation
Publication TypePresentation
Year of Publication2023
AuthorsHuseyin Tuna Erdinc, Abhinav Prakash Gahlot, Mathias Louboutin, Felix J. Herrmann
KeywordsCCS, classifier, deep learning, leakage detection, ML4SEISMIC, SLIM

Previous works showed that neural classifiers can be trained to detect CO2 leakage from time-lapse seismic images. While this result is crucial to the global deployment of geological carbon storage (GCS), its success depends on relatively dense non-replicated time-lapse data acquisition. In this study, we present an approach to enhance the detection accuracy and robustness of CO2 leakage detection by augmenting the training dataset with a variety of coarsely sampled receiver data and their corresponding receiver numbers. This augmentation strategy is particularly beneficial for scenarios where low-cost coarse receiver samplings, such as with ocean bottom nodes (OBNs), are utilized. Furthermore, we explore interpretability of the classifier's decisions by generating saliency maps for further analysis.

Citation Keyerdinc2023ML4SEISMICias