Enhancing CO2 Leakage Detectability via Dataset Augmentation
Title | Enhancing CO2 Leakage Detectability via Dataset Augmentation |
Publication Type | Conference |
Year of Publication | 2023 |
Authors | Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Mathias Louboutin, Felix J. Herrmann |
Conference Name | International Meeting for Applied Geoscience and Energy |
Month | 08 |
Keywords | CCS, classifier, deep learning, leakage detection, SEG |
Abstract | Previous work 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 show that by augmenting the training set with various coarse receiver samplings and corresponding seismic images, we can improve the leakage detection capabilities and accuracy while increasing the robustness with respect to low-cost coarse receiver samplings, e.g. ocean bottom nodes (OBNs). |
Notes | (IMAGE, Houston) |
URL | https://slimgroup.github.io/IMAGE2023/DetectabilityWithVision/abstract.html |
Presentation | |
Citation Key | erdinc2023IMAGEecl |