  Geosystems are geological formations altered by humans activities such as
fossil energy exploration, waste disposal, geologic carbon sequestration, and
renewable energy generation. Geosystems also represent a critical link in the
global water-energy nexus, providing both the source and buffering mechanisms
for enabling societal adaptation to climate variability and change. The
responsible use and exploration of geosystems are thus critical to the
geosystem governance, which in turn depends on the efficient monitoring, risk
assessment, and decision support tools for practical implementation. Fast
advances in machine learning (ML) algorithms and novel sensing technologies in
recent years have presented new opportunities for the subsurface research
community 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 of
geosystems, which are typified by multiscality, high-dimensionality, and data
resolution inhomogeneity. This survey will provide a systematic review of the
recent development and applications of domain-aware SciML in geosystem
researches, with an emphasis on how the accuracy, interpretability,
scalability, defensibility, and generalization skill of ML approaches can be
improved to better serve the geoscientific community.
