论文标题
在延时地震监测图像中，可解释的二氧化碳泄漏检测的碳捕获和隔离的碳捕获和隔离
De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images
论文作者
论文摘要
随着碳捕获和隔离技术的全球部署日益增长，通过现有或存储诱导的故障来监测和检测潜在的CO2泄漏，这对于该技术的安全和长期可行性至关重要。最近对二氧化碳存储的延时地震监测的工作表现出了令人鼓舞的结果，其能够从表面记录的地震数据中监测二氧化碳羽流的生长。但是，由于地震成像对二氧化碳浓度的灵敏度较低，因此需要进行其他发展来有效地解释地震图像以泄漏。在这项工作中，我们介绍了延时地震图像的二进制分类，以使用最先进的深度学习模型来描述CO2羽毛（泄漏）。此外，我们通过利用类激活映射方法来定位CO2羽流的泄漏区域。
With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 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 CO2 storage has shown promising results in its ability to monitor the growth of the CO2 plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO2 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 CO2 plumes (leakage) using state-of-the-art deep learning models. Additionally, we localize the leakage region of CO2 plumes by leveraging Class Activation Mapping methods.
