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&#...
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| Veröffentlicht in: | Science (American Association for the Advancement of Science) 2019-03, Vol.363 (6433) |
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| container_title | Science (American Association for the Advancement of Science) |
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| creator | Bergen, Karianne J Johnson, Paul A de Hoop, Maarten V Beroza, Gregory C |
| description | 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. |
| doi_str_mv | 10.1126/science.aau0323 |
| format | Article |
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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. 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Johnson, Paul A ; de Hoop, Maarten V ; Beroza, Gregory C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a482t-c4450a8ee48caa0f1a37866e8711e6340c7e1993f044639159d0ebac368c3ddd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Acoustic emission</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Atmospheric models</topic><topic>Automation</topic><topic>Benchmarks</topic><topic>Biological activity</topic><topic>Classification</topic><topic>Communities</topic><topic>Computer memory</topic><topic>Computer simulation</topic><topic>Computers</topic><topic>Data processing</topic><topic>Data sources</topic><topic>Datasets</topic><topic>Driving ability</topic><topic>Earth science</topic><topic>Earthquakes</topic><topic>Fiber optics</topic><topic>Fresh water</topic><topic>Freshwater environments</topic><topic>Geologic mapping</topic><topic>Groundwater</topic><topic>Groundwater reservoirs</topic><topic>Inverse problems</topic><topic>Learning algorithms</topic><topic>Lidar</topic><topic>Machine learning</topic><topic>Mathematical models</topic><topic>Mineral resources</topic><topic>Oceans</topic><topic>Open data</topic><topic>Optical fibers</topic><topic>Organic chemistry</topic><topic>Predictions</topic><topic>Remote sensing</topic><topic>Rheology</topic><topic>Seismic activity</topic><topic>Teaching Methods</topic><topic>Topology</topic><topic>Urbanization</topic><topic>Viscoelasticity</topic><topic>Volcanic ash</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bergen, Karianne J</creatorcontrib><creatorcontrib>Johnson, Paul A</creatorcontrib><creatorcontrib>de Hoop, Maarten V</creatorcontrib><creatorcontrib>Beroza, Gregory C</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>OSTI.GOV</collection><jtitle>Science (American Association for the Advancement of Science)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bergen, Karianne J</au><au>Johnson, Paul A</au><au>de Hoop, Maarten V</au><au>Beroza, Gregory C</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine learning for data-driven discovery in solid Earth geoscience</atitle><jtitle>Science (American Association for the Advancement of Science)</jtitle><addtitle>Science</addtitle><date>2019-03-22</date><risdate>2019</risdate><volume>363</volume><issue>6433</issue><issn>0036-8075</issn><eissn>1095-9203</eissn><abstract>Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. 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| 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 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 Rheology Seismic activity Teaching Methods Topology Urbanization Viscoelasticity Volcanic ash |
| title | Machine learning for data-driven discovery in solid Earth geoscience |
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