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&#...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Science (American Association for the Advancement of Science) 2019-03, Vol.363 (6433), p.1299-1299
Hauptverfasser:	Bergen, Karianne J., Johnson, Paul A., de Hoop, Maarten V., Beroza, Gregory C.
Format:	Artikel
Sprache:	eng
Schlagworte:	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 science Earthquakes Fiber optics Fresh water Freshwater environments Geologic mapping 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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!