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Activity Number: 541
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #319074 View Presentation
Title: Clustering of Soil Geochemical and Mineralogical Data from the Conterminous United States Using a Bayesian Finite Mixture Model
Author(s): Karl Ellefsen* and Laurel Woodruff and David Smith and William Cannon and Federico Solano
Companies: U.S. Geological Survey and U.S. Geological Survey and U.S. Geological Survey and U.S. Geological Survey and U.S. Geological Survey
Keywords: finite mixture model ; clustering ; soil geochemical data ; soil mineralogical data

In 2013, the U.S. Geological Survey completed a geochemical and mineralogical soil survey of the conterminous United States. At 4,857 locations, soil samples were collected at three depth levels; for each sample, the concentrations of 45 elements and 20 minerals were measured. To aid interpretation of these multivariate, compositional data sets, statistical clustering was used. The clustering procedure partitioned the field samples for a data set into two clusters. Each cluster was partitioned again to create two sub-clusters, and so on, generating a hierarchy of clusters. The different levels of the hierarchy indicated different influences of the soil-forming processes at varying spatial scales. The clustering method was based on a Bayesian finite mixture model. The model parameters were estimated with Hamiltonian Monte Carlo sampling of the posterior probability density function, which usually had multiple modes. Each mode was associated with a unique set of model parameters. The set that was most consistent with the independent geologic knowledge was selected for detailed interpretation and further partitioning of the data.

Authors who are presenting talks have a * after their name.

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