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Geosystems are geological formations altered by humans activities such asfossil energy exploration, waste disposal, geologic carbon sequestration, andrenewable energy generation. Geosystems also represent a critical link in theglobal water-energy nexus, providing both the source and buffering mechanismsfor enabling societal adaptation to climate variability and change. Theresponsible use and exploration of geosystems are thus critical to thegeosystem governance, which in turn depends on the efficient monitoring, riskassessment, and decision support tools for practical implementation. Fastadvances in machine learning (ML) algorithms and novel sensing technologies inrecent years have presented new opportunities for the subsurface researchcommunity to improve the efficacy and transparency of geosystem governance.Although recent studies have shown the great promise of scientific ML (SciML)models, questions remain on how to best leverage ML in the management ofgeosystems, which are typified by multiscality, high-dimensionality, and dataresolution inhomogeneity. This survey will provide a systematic review of therecent development and applications of domain-aware SciML in geosystemresearches, with an emphasis on how the accuracy, interpretability,scalability, defensibility, and generalization skill of ML approaches can beimproved to better serve the geoscientific community.

Applications of physics-informed scientific machine learning in subsurface science: A survey