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- Title
Probabilistic Reconstruction of Hydrofacies With Support Vector Machines.
- Authors
Dendumrongsup, Nutchapol; Tartakovsky, Daniel M.
- Abstract
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 them to delineate, from lithological data collected in a number of wells, the spatial extent of an aquitard separating two aquifers in Southern California. Key Points: We propose probabilistic Support Vector Machines (pSVM) to reconstruct hydrofacies from sparse data and to quantify predictive uncertaintypSVM generate smoother probability maps than those produced by indicator Kriging (IK), that is, pSVM provide more conservative estimatespSVM are preferable to IK because the latter has more tunable parameters and higher data requirements
- Subjects
SOUTHERN California; SUPPORT vector machines; AQUIFERS; KRIGING
- Publication
Water Resources Research, 2021, Vol 57, Issue 5, p1
- ISSN
0043-1397
- Publication type
Article
- DOI
10.1029/2021WR029622