@unpublished {gahlot2025EAGEerd,
	title = {Enhancing robustness of Digital Shadow for CO 2  Storage Monitoring with Augmented Rock Physics Modeling},
	year = {2025},
	month = {6},
	keywords = {Amortized Variational Inference, augmentation, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, FWI, GCS, Imaging, Inverse problems, rock physics, RTM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2025/gahlot2025EAGEerd/abstract.html},
	author = {Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@presentation {zeng2024ML4SEISMICepu,
	title = {Enhancing Performance with Uncertainty Estimation in Geological Carbon Storage Leakage Detection from Time-Lapse Seismic Data},
	year = {2024},
	month = {11},
	abstract = {Ensuring CO 2  non-leakage is a critical aspect of Geological Carbon Storage (GCS). While previous approaches that develop deep neural networks demonstrate promising automatic leakage detection and potential cost reduction in dataset collection from time-lapse seismic images, they face challenges, such as a limited ability to reduce false alarms in CO 2  leakage instances and a lack of uncertainty analysis in detection results. This paper introduced a framework aimed at enhancing the deep neural network model{\textquoteright}s ability to detect GCS leakage risk through a multi-criteria decision-making (MCDM)-based ensemble algorithm. The proposed method can improve the detection ability of leakage cases while accurately distinguishing them from non-leakage instances. Furthermore, the proposed uncertainty analysis method utilizing Monte Carlo (MC) dropout technique efficiently identifies misclassified non-leakage cases and categorizes them as undetermined for further investigation. This comprehensive approach enhances both the reliability and performance of the model in detecting GCS leakage risks.},
	keywords = {CAM, CCS, classification, explainability, GCS, Imaging, Inverse problems, JRM, ML4SEISMIC, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/zeng2024ML4SEISMICepu},
	author = {Shiqin Zeng and Huseyin Tuna Erdinc and Ziyi Yin and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {erdinc2023IMAGEecl,
	title = {Enhancing CO2 Leakage Detectability via Dataset Augmentation},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	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).},
	keywords = {CCS, classifier, deep learning, leakage detection, SEG},
	url = {https://slimgroup.github.io/IMAGE2023/DetectabilityWithVision/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/erdinc2023IMAGEecl},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
