Applying regularized logistic regression (RLR) for the discrimination of sediment facies in reservoirs based on composite fingerprints

Autor: Christoph Glotzbach, Jordan Miller, Kate Rowntree, Bastian Reinwarth, Jussi Baade
Rok vydání: 2017
Předmět:
Zdroj: Journal of Soils and Sediments. 17:1777-1795
ISSN: 1614-7480
1439-0108
Popis: Soils and sediments can be distinguished based on “composite fingerprints”, i.e., sets of physical and chemical properties that are suitable for discrimination. At present, statistical stepwise variable selection methods are frequently applied to identify composite fingerprints, although they have been seriously criticized. Here, we test regularized logistic regression (RLR) as an alternative approach in the context of a reservoir siltation study where the post-dam facies is to be distinguished from the pre-dam facies. The pre- and post-dam facies of four reservoirs located in the Kruger National Park were examined with respect to grain size composition, color, and content of calcium-lactate leachable phosphorus (P CAL). A composite fingerprint was identified applying RLR to training data. The fitted regression model was used for the classification of samples not involved in the training dataset. For comparison, variable selection was performed with stepwise discriminant function analysis (DFA) and samples were classified by applying linear discriminant analysis (LDA). Both approaches were validated by comparing field interpretation and classification results. The analysis was extended based on Monte Carlo simulations and synthetic datasets to quantify uncertainties and to enhance the method comparison. RLR and stepwise DFA identify grain size parameters and P CAL content to be particularly useful for the facies discrimination. Neglecting and taking into account a potential sampling bias, both approaches lead to ≤3 and 5% misclassifications, respectively. RLR outperforms stepwise DFA/LDA in Monte Carlo simulations, although misclassification rates do not significantly differ (p = 0.84). RLR uses on average 12% less fingerprint properties. Moreover, RLR-derived probabilities of group membership represent a more reliable measure for classification conclusiveness than probabilities calculated from LDA, which is evident in significantly lower (p
Databáze: OpenAIRE
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