Remote Sensing and Machine Learning Framework for Natural Hydrogen Exploration

Abstract This study explores the application of remote sensing and machine learning techniques to detect surface indicators of hydrogen seepage in the Proterozoic Arabian Shield, with a focus on ophiolite formations. Utilizing Sentinel-2 satellite imagery from the European Space Agency and Digital Elevation Models (DEMs), we applied geospatial analysis and advanced deep learning algorithms to identify fairy circles and other circular surface anomalies potentially linked to subsurface hydrogen migration and surface seepage. The results demonstrate that machine learning can effectively detect these features across large areas, providing valuable insights into regions that may warrant further field investigation and validation. However, the study also highlights the need for higher-resolution imagery and high-resolution DEMs to improve detection accuracy, as well as the computational challenges of handling large datasets. The findings emphasize the potential of integrating remote sensing with machine learning to enhance the efficiency and effectiveness of hydrogen seepage exploration, offering a novel approach for large-scale geological surveys in regions with potential subsurface hydrogen resources.