# De-risking Carbon Capture and Sequestration with Explainable CO$_2$ Leakage Detection in Time-lapse Seismic Monitoring Images

 Title De-risking Carbon Capture and Sequestration with Explainable CO$_2$ Leakage Detection in Time-lapse Seismic Monitoring Images Publication Type Conference Year of Publication 2022 Authors Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Ziyi Yin, Mathias Louboutin, Felix J. Herrmann Conference Name AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges Month 08 Keywords AAAI, CAM, CCS, classification, explainability, JRM, seismic imaging Abstract With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO$_2$ leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO$_2$ storage has shown promising results in its ability to monitor the growth of the CO$_2$ plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO$_2$ concentration, additional developments are required to efficiently interpret the seismic images for leakage. In this work, we introduce a binary classification of time-lapse seismic images to delineate CO$_2$ plumes (leakage) using state-of-the-art deep learning models. Additionally, we localize the leakage region of CO$_2$ plumes by leveraging Class Activation Mapping (CAM) methods. Notes (AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges, Arlington) URL https://slim.gatech.edu/Publications/Public/Conferences/AAAI/2022/erdinc2022AAAIdcc/erdinc2022AAAIdcc.pdf Citation Key erdinc2022AAAIdcc