Machine learning for data-driven discovery in solid Earth geoscience

Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. It is made challenging by the complex, interacting, and multiscale processes needed to understand Earth's behavior and by the inaccessibility of nearly all of Earth's subsurface to direct observation. Substantial increases in data availability and in the increasingly realistic character of computer simulations hold promise for accelerating progress, but developing a deeper understanding based on these capabilities is itself challenging. Machine learning will play a key role in this effort. We review the state of the field and make recommendations for how progress might be broadened…

• NS
  National Science Foundation

  Awards: DMS-1815143, EAR-1818579
• UD
  U.S. Department of Energy

  Award: KC030206
• SF
  Simons Foundation
• HD
  Harvard Data Science Initiative, Harvard University
• LA
  Los Alamos National Laboratory