Bergen Karianne J, Johnson Paul A, de Hoop Maarten V, Beroza Gregory C
Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA.
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, USA.
Science. 2019 Mar 22;363(6433). doi: 10.1126/science.aau0323.
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 and accelerated.
通过固体地球地球科学的不同领域来理解地球的行为是一项日益重要的任务。由于理解地球行为所需的复杂、相互作用和多尺度过程，以及几乎所有地球地下区域都难以进行直接观测，这项任务极具挑战性。数据可用性的大幅增加以及计算机模拟日益逼真的特性有望加速进展，但基于这些能力形成更深入的理解本身也具有挑战性。机器学习将在这项工作中发挥关键作用。我们回顾了该领域的现状，并就如何扩大和加速进展提出建议。
