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DOI10.1029/2021WR029622
Probabilistic Reconstruction of Hydrofacies with Support Vector Machines
Nutchapol Dendumrongsup; Daniel M. Tartakovsky
2021-04-23
发表期刊Water Resources Research
出版年2021
英文摘要

Delineation of geological features from limited hard and/or soft data is crucial to predicting subsurface phenomena. Ubiquitous sparsity of available data implies that the reliability of any delineation effort is inherently uncertain. We present probabilistic support vector machines (pSVM) as a viable method for both hydrofacies delineation from sparse data and quantification of the corresponding predictive uncertainty. Our numerical experiments with synthetic data demonstrate an agreement between the probability of a pixel classifier predicted with pSVM and indicator Kriging. While the latter requires manual inference of a variogram (two‐point correlation function) from spatial observations, pSVM are highly automated and less data intensive. We also investigate the robustness of pSVM with respect to its hyper‐parameters and the number of measurements. Having investigated these features of pSVM, we deploy it to delineate, from lithological data collected in a number of wells, the spatial extent of an aquitard separating two aquifers in Southern California.

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文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/324027
专题资源环境科学
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Nutchapol Dendumrongsup,Daniel M. Tartakovsky. Probabilistic Reconstruction of Hydrofacies with Support Vector Machines[J]. Water Resources Research,2021.
APA Nutchapol Dendumrongsup,&Daniel M. Tartakovsky.(2021).Probabilistic Reconstruction of Hydrofacies with Support Vector Machines.Water Resources Research.
MLA Nutchapol Dendumrongsup,et al."Probabilistic Reconstruction of Hydrofacies with Support Vector Machines".Water Resources Research (2021).
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