Data-driven approaches for sustainable geoscience: Machine learning in mineral resources, water management, and environmental resilience

Machine learning has rapidly become integral to the advancement of geoscience, a field inundated with complex and multivariate data from myriad sources such as geological surveys, geophysical measurements, geochemical analyses, remote sensing, and hydrogeological monitoring. The evolution of these technologies is reshaping our ability to analyze and interpret large-scale, heterogeneous datasets, thereby contributing to understanding fundamental Earth processes and solving applied problems. However, the limitations of conventional analytical techniques are apparent, particularly in their inability to harness the full potential of modern geoscientific data. Recent studies have demonstrated the ability of machine learning to deliver robust pattern recognition, predictive modeling, and uncertainty quantification for solving challenges in mineral exploration, groundwater management, and environmental monitoring. These data-driven advances are occurring amid increasing societal emphasis on sustainability and the responsible management of natural resources, spurred by global pressures related to climate change, resource scarcity, and environmental protection.Despite promising technological gains, the integration of machine learning into geoscience remains challenged by issues such as fragmented methodological approaches, limited reproducibility, and a lack of focus on ethical and sustainable outcomes. Current research reveals that, while machine learning offers powerful predictive and interpretive capabilities, many applications lack integration with geological domain knowledge, transparency, or consideration of broader environmental and societal factors. Key investigations have begun to address these gaps by developing reproducible workflows, interpretable models, and frameworks that place sustainability and risk mitigation at the forefront. Nevertheless, the field needs a more cohesive and sustainability-driven direction to unlock the full value of machine learning in addressing resource management, water security, and environmental resilience.This Research Topic aims to foster the development and dissemination of innovative machine learning applications in geoscience that emphasize sustainability, transparent methodologies, and societal relevance. Specifically, the objective is to highlight original contributions that bridge the gap between data-centric models and geoscientific expertise, offering interpretable and reproducible tools for mineral exploration, groundwater assessment, environmental monitoring, and climate adaptation. It also seeks to foreground the evaluation of machine learning’s limitations, uncertainties, and ethical considerations, thereby promoting responsible practices and informed decision-making for the stewardship of Earth’s resources.The scope of this Research Topic is limited to original and methodologically rigorous studies that apply machine learning to sustainable outcomes in geoscience, focusing on real-world datasets and practical insights. Research that advances transparency, reproducibility, and ethical responsibility is particularly encouraged. To gather further insights in this evolving field, we welcome articles addressing, but not limited to, the following themes:o Integration of geophysical, geochemical, and remote sensing data for resource discoveryo Machine learning–enabled prospectivity mapping and risk-aware strategies in mineral explorationo Predictive modeling for groundwater availability, quality, and climate change impactso Early warning systems and decision-support tools for sustainable hydrogeologyo Applications in pollution monitoring, hazard assessment, and climate–environment interactionso Interpretable, transparent, and reproducible machine learning workflows in geoscienceo Evaluation of uncertainties, biases, and ethical considerations in geoscientific AIAppendix: The Research Topic welcomes original research articles, review and synthesis papers, and case studies based on real-world data. Topic editors: Raymond Webrah Kazapoe, Daniel Kwayisi, Clifford Broni-Bediako, Tersoo Aga Source: https://www.frontiersin.org/research-topics/85029/data-driven-approaches-for-sustainable-geoscience-machine-learning-in-mineral-resources-water-management-and-environmental-resilience