AI-assisted Remote Sensing Screening of Potential Natural Hydrogen Seepage Features in Alta Guajira, Northern Colombia

Natural (geological) hydrogen refers to molecular hydrogen produced in the subsurface through abiotic and biogenic pathways, which may migrate, accumulate transiently, be consumed by secondary reactions, or escape to the surface. Increasing evidence indicates that such systems could be a strategic low-carbon energy source, but their exploration is limited as regional-scale, data-driven approaches to identify mechanisms of active or fossil migration in geologically complex environments are lacking. Surface expressions such as circular and sub-circular depressions associated with soil and vegetation anomalies have been reported worldwide as indirect indicators of hydrogen migration and leakage. However, their detection remains limited to either local reconnaissance of the field or manual interpretation of remote-sensing data. In this research, we present an AI-assisted remote sensing framework to conduct a regional screening based on the potential for natural hydrogen seepage patterns to enhance early-stage exploration and improve the quantitative characterization of surface indicators linked to subsurface energy systems. Deep-learning–based computer vision models are used to study high-resolution satellite imagery and automatically identify and classify circular and sub-circular geomorphological features that could correspond to hydrogen exudation. The resulting detections are integrated into a GIS framework for the extraction of morphometric and spatial statistics, providing a formal analytical benchmark to relate surface structures to lithology, structural configuration, and the regional tectonic setting. The workflow is applied to the Alta Guajira region (in northern Colombia), a geologically complex segment of the Caribbean margin characterized by accreted oceanic crust, major fault systems, and sedimentary depocenters that may favor hydrogen generation and migration. Using an AI-based approach allows the construction of a regional inventory of candidate seepage-related structures while significantly reducing false positives associated with purely morphology-based analyses. The results support the prioritization of targets for future field verification, geochemical sampling, and subsurface investigations. Beyond its implications for natural hydrogen prospectivity, the proposed methodology demonstrates how artificial intelligence can translate qualitative geological observations into quantitative, reproducible screening tools. By providing a transparent and spatially explicit representation of subsurface energy systems, AI-assisted screening also facilitates communication with stakeholders and local communities, contributing to informed public perception of emerging sustainable subsurface energy resources in data-limited regions such as Alta Guajira.

The researchers thank the SHATKI Research Project (code 110563), Contingent Recovery Contract No. 112721-042-2025, funded by the Ministry of Science, Technology and Innovation (Minciencias) and the National Hydrocarbons Agency (ANH).