Machine Learning for the Geosciences: Challenges and Opportunities

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 p...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 31; H. 8; S. 1544 - 1554
Hauptverfasser:	Karpatne, Anuj, Ebert-Uphoff, Imme, Ravela, Sai, Babaie, Hassan Ali, Kumar, Vipin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial intelligence Atmospheric modeling Cross cutting Data models Domains Earth earth observation data Earth science Geology geoscience Machine learning Machine tools Meteorology physics-based models Sensors
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
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Abstract	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.
AbstractList	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.
Author	Karpatne, Anuj Ebert-Uphoff, Imme Babaie, Hassan Ali Ravela, Sai Kumar, Vipin
Author_xml	– sequence: 1 givenname: Anuj orcidid: 0000-0003-1647-3534 surname: Karpatne fullname: Karpatne, Anuj email: karpa009@umn.edu organization: University of Minnesota, Minneapolis, MN, USA – sequence: 2 givenname: Imme orcidid: 0000-0001-6470-1947 surname: Ebert-Uphoff fullname: Ebert-Uphoff, Imme email: iebert@colostate.edu organization: Colorado State University, Fort Collins, CO, USA – sequence: 3 givenname: Sai surname: Ravela fullname: Ravela, Sai email: ravela@mit.edu organization: Massachusetts Insititue of Technology, Cambridge, MA, USA – sequence: 4 givenname: Hassan Ali surname: Babaie fullname: Babaie, Hassan Ali email: hbabaie@gsu.edu organization: Georgia State University, Atlanta, GA, USA – sequence: 5 givenname: Vipin surname: Kumar fullname: Kumar, Vipin email: kumar001@umn.edu organization: University of Minnesota, Minneapolis, MN, USA
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