Machine Learning for the Geosciences: Challenges and Opportunities

TL;DR

This paper discusses the unique challenges and opportunities of applying machine learning to geosciences, emphasizing the need for novel methods and interdisciplinary collaboration to address societal and environmental issues.

Contribution

It introduces the specific challenges of geoscience data for machine learning and highlights promising directions for methodological development and cross-disciplinary collaboration.

Findings

Geoscience data has unique properties that complicate traditional ML applications.

Machine learning can significantly advance geoscience problem-solving.

Emerging research themes foster interdisciplinary progress.

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, problems in geosciences have several unique challenges that are seldom found in traditional applications, requiring novel problem formulations and methodologies 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 properties that make it challenging to use traditional machine learning techniques. We then describe some of the common categories of geoscience problems where machine learning can play a role, and discuss some of the existing efforts and promising directions for methodological development in machine learning. We conclude by discussing some of the emerging research themes in machine learning that are applicable across all problems in the geosciences, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines.