Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information
نویسندگان
چکیده
Abstract Earth scientists increasingly deal with ‘big data’. For spatial interpolation tasks, variants of kriging have long been regarded as the established geostatistical methods. However, and its (such regression kriging, in which auxiliary variables or derivatives these are included covariates) relatively restrictive models lack capabilities provided by deep neural networks. Principal among is feature learning: ability to learn filters recognise task-relevant patterns gridded data such images. Here, we demonstrate power learning a context showing how networks can automatically complex high-order point-sampled target relate those remote sensing) doing so produce detailed maps. In order cater for needs decision makers who require well-calibrated probabilities, also both aleatoric epistemic uncertainty be quantified our approach via Bayesian approximation known Monte Carlo dropout. example, national-scale probabilistic geochemical map from observations terrain elevation grid. By combining location information learned derivatives, achieves an excellent coefficient determination ( $$R^{2} = 0.74$$ R 2 = 0.74 ) near-perfect calibration on held-out test data. Our results indicate suitability feature-learning large-scale applications where matters. Graphic
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ژورنال
عنوان ژورنال: Mathematical Geosciences
سال: 2022
ISSN: ['1874-8961', '1874-8953']
DOI: https://doi.org/10.1007/s11004-021-09988-0