Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information

Earth scientists increasingly deal with ‘big data’. For spatial interpolation tasks, variants of kriging have long been regarded as the established geostatistical methods. However, kriging and its variants (such as regression kriging, in which auxiliary variables or derivatives of these are included...

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Veröffentlicht in:	Mathematical geosciences Jg. 54; H. 3; S. 507 - 531
Hauptverfasser:	Kirkwood, Charlie, Economou, Theo, Pugeault, Nicolas, Odbert, Henry
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2022 Springer Nature B.V
Schlagworte:	Approximation Artificial neural networks Bayesian analysis Bayesian theory Big Data Chemistry and Earth Sciences Computer Science Deep learning Earth and Environmental Science Earth Sciences Elevation Geostatistics Geotechnical Engineering & Applied Earth Sciences Hydrogeology Interpolation Machine learning Mathematical models Neural networks Pattern recognition Physics Probability theory Remote sensing Spatial data Spatial discrimination learning Special Issue Statistical analysis Statistical methods Statistics for Engineering Terrain Uncertainty Feature learning Uncertainty quantification Mapping Neural networks Geostatistics Machine learning
ISSN:	1874-8961, 1874-8953
Online-Zugang:	Volltext
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