GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds

The increasing availability of large geological datasets and modern methods of data analysis facilitate a data science approach to geology in which inferences are drawn from geological data using automated methods based on statistics and machine learning. Such methods offer the potential for faster...

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Veröffentlicht in:	Geoscientific Model Development Jg. 18; H. 4; S. 939 - 960
Hauptverfasser:	Oakley, David, Loiselet, Christelle, Coowar, Thierry, Labbe, Vincent, Callot, Jean-Paul
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Katlenburg-Lindau Copernicus GmbH 19.02.2025 European Geosciences Union Copernicus Publications
Schlagworte:	Algorithms Analysis Artificial neural networks Automation Clustering Data analysis Data science Datasets Earth Sciences Fault lines Folds Geological data Geological mapping Geological maps Geologists Geology Identification Learning algorithms Machine learning Neural networks Sciences of the Universe Statistical analysis Statistical methods Structures Supervised learning Topography Unsupervised learning
ISSN:	1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X, 1991-959X
Online-Zugang:	Volltext
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