Machine learning for data-driven discovery in solid Earth geoscience

• Published in: Science (American Association for the Advancement of Science) Vol. 363; no. 6433; p. 1299
• Main Authors: Bergen, Karianne J., Johnson, Paul A., de Hoop, Maarten V., Beroza, Gregory C.
• Format: Journal Article
• Language: English
• Published: United States American Association for the Advancement of Science 22.03.2019; The American Association for the Advancement of Science; American Association for the Advancement of Science (AAAS)
• Subjects: Acoustic emission; Algorithms; Artificial intelligence; Atmospheric models; Automation; Benchmarks; Biological activity; Classification; Communities; Computer memory; Computer simulation; Computers; Data processing; Data sources; Datasets; Driving ability; Earth; Earth science; Earthquakes; Fiber optics; Fresh water; Freshwater environments; Geologic mapping; Geological hazards; Groundwater; Groundwater reservoirs; Inverse problems; Learning algorithms; Lidar; Machine learning; Mathematical models; Mineral resources; Oceans; Open data; Optical fibers; Organic chemistry; Predictions; Remote sensing; REVIEW SUMMARY; Rheology; Seismic activity; Teaching Methods; Topology; Urbanization; Viscoelasticity; Volcanic ash

Solid Earth geoscience is a field that has very large set of observations, which are ideal for analysis with machine-learning methods. Bergen et al. review how these methods can be applied to solid Earth datasets. Adopting machine-learning techniques is important for extracting information and for u...