Machine Learning for the Geosciences: Challenges and Opportunities

By Anuj Karpatne, I. Ebert‐Uphoff, S. Ravela, Hassan A. Babaie, Vipin Kumar. IEEE Transactions on Knowledge and Data Engineering, 2017

521 citations recorded by the source corpus.

Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data, machine learning (ML)—that has been widely successful in commercial domains—offers immense potential to contribute to problems in geosciences. However, geoscience applications introduce novel challenges for ML due to combinations of geoscience properties encountered in every problem, requiring novel research in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by geoscience problems and the opportunities that exist for advancing both machine learning and geosciences. We first highlight typical sources of geoscience data and describe their common properties. We then describe some of the common categories of geoscience problems where machine learning can play a role, discussing the challenges faced by existing ML methods and opportunities for novel ML research. We conclude by discussing some of the cross-cutting research themes in machine learning that are applicable across several geoscience problems, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines.

Subfields: Artificial Intelligence, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/05d20fda297c9afb347214bd1693bd049674e0c6

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Citations in the corpus

Explainable Machine Learning for Scientific Insights and Discoveries (2019, background): “Moreover, [115] observes that a close collaboration with domain experts in the geoscientific area and ML researchers is necessary to solve novel and relevant tasks.”
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks (2019, background): “…applicable across climate science and ML problems, which requires deep collaboration for synergistic advancements in both disciplines (Monteleoni et al., 2013; Joppa, 2017; Racah et al., 2017; Schneider et al., 2017; Gil et al., 2018; Hwang et al., 2018; Karpatne et al., 2018; Rasp et al., 2018).”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “These constraints and similar physics-guided techniques have the potential to alleviate noted challenges in inverse modeling, particularly in scenarios with a small sample size and a paucity of ground-truth labels [142].”
Riemannian Score-Based Generative Modeling (2022, background): “…and Dunbrack Jr, 2011; Mardia et al., 2007), cell development (Klimovskaia et al., 2020), image recognition (Lui, 2012), geological sciences (Karpatne et al., 2018; Peel et al., 2001), graph-structured and hierarchical data (Roy et al., 2007; Steyvers and Tenenbaum, 2005), robotics (Feiten…”
Machine learning for data-driven discovery in solid Earth geoscience (2019, unknown): “( 143 )].”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “Machine learning (ML) models, which have already found tremendous success in commercial applications, are beginning to play an important role in advancing scientific discovery in domains traditionally dominated by mechanistic (e.g. first principle) models [22, 112, 117, 126, 127, 138, 214, 261].”