  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.
