title: Data-driven modeling of background and mine-related acidity and metals in river basins.
abstract: A novel application of self-organizing map (SOM) and multivariate statistical techniques is used to model the nonlinear interaction among basin mineral-resources, mining activity, and surface-water quality. First, the SOM is trained using sparse measurements from 228 sample sites in the Animas River Basin, Colorado. The model performance is validated by comparing stochastic predictions of basin-alteration assemblages and mining activity at 104 independent sites. The SOM correctly predicts (>98%) the predominant type of basin hydrothermal alteration and presence (or absence) of mining activity. Second, application of the Davies-Bouldin criteria to k-means clustering of SOM neurons identified ten unique environmental groups. Median statistics of these groups define a nonlinear water-quality response along the spatiotemporal hydrothermal alteration-mining gradient. These results reveal that it is possible to differentiate among the continuum between inputs of background and mine-related acidity and metals, and it provides a basis for future research and empirical model development.
authors.affiliation: Crustal Geophysics and Geochemistry Science Center, U.S. Geological Survey, Denver Federal Center, Box 25046, MS 964, Lakewood, CO 80225, USA; Center for Computational and Mathematical Biology, University of Colorado, Campus Box 170, PO Box 173364, Denver, CO 80217-3364, USA. Electronic address: mfriedel@usgs.gov.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Friedel
mesh_terms: D003120:Colorado; D004784:Environmental Monitoring / Q000379:methods*; D008670:Metals / Q000032:analysis*; D008906:Mining; D008956:Models, Chemical; D045483:Rivers / Q000737:chemistry*; D014874:Water Pollutants, Chemical
chemical_list: D008670:Metals; D014874:Water Pollutants, Chemical
keywords: Cluster analysis; Environmental assessment; Hydrothermal alteration; Mineral-resource assessment; Mining activity; Self-organizing map; Stochastic modeling; Uncertainty; Water quality