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National-scale geologic, geophysical, and mineral resource raster and vector data covering the United States, Canada, and Australia are provided in this data release. The data were compiled as part of the tri-national Critical Minerals Mapping Initiative (CMMI). The CMMI, established in 2019, is an international science collaboration between the U.S. Geological Survey (USGS), Geoscience Australia (GA), and the Geological Survey of Canada (GSC). One aspect of the CMMI is to use national- to global-scale earth science data to map where critical mineral prospectivity may exist using advanced machine learning approaches (Kelley, 2020). The geoscience information presented in this report include the training and evidential...
Categories: Data; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: ARDS, Alaska, Alaska Mineral Resource Data, Australia, Canada, All tags...
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An extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in shallow groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution at a depth below the water table of 10 m. The model builds off a previous XGB machine learning model developed to predict nitrate at domestic and public supply groundwater zones (Ransom and others, 2022) by incorporating additional monitoring well samples and modifying and adding predictor variables. The shallow zone model included variables representing well characteristics, hydrologic conditions, soil type, geology, climate, oxidation/reduction, and nitrogen inputs. Predictor...
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GeoTiff grids of models of prospectivity for clastic-dominated (CD) and Mississippi Valley-type (MVT) Pb-Zn mineralization for the US and Canada (combined) and Australia that used data provided in this report are provided here. The models are the result of a study by Lawley and others (2022) that used a data-driven machine learning approach called Gradient Boosting to predict the mineral prospectivity for clastic-dominated (CD) and carbonate-hosted (MVT) deposits across the United States, Canada, and Australia. The study was part of a tri-national collaboration between the U.S. Geological Survey, the Canadian Geological Survey, and Geoscience Australia called the Critical Minerals Mapping Initiative. The original...
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Groundwater from the Mississippi River Valley alluvial aquifer (MRVA), coincident with the Mississippi Alluvial Plain (MAP), is a vital resource for agriculture and drinking-water supplies in the central United States. Water availability can be limited in some areas of the aquifer by high concentrations of salinity, measured as specific conductance. Boosted regression trees (BRT), a type of ensemble-tree machine-learning method, were used to predict specific conductance concentration at multiple depths throughout the MRVA and underlying aquifers. Two models were created to test the incorporation of datasets from a regional aerial electromagnetic (AEM) survey and evaluate model performance. Explanatory variables...
    map background search result map search result map Machine-learning model predictions and rasters of groundwater salinity in the Mississippi Alluvial Plain National-Scale Geophysical, Geologic, and Mineral Resource Data and Grids for the United States, Canada, and Australia: Data in Support of the Tri-National Critical Minerals Mapping Initiative [Prospectivity Models] Prospectivity models - clastic-dominated (CD) and Mississippi Valley-type (MVT) GeoTIFF grids for the United States, Canada, and Australia Data for Machine Learning Predictions of Nitrate in Shallow Groundwater in the Conterminous United States Machine-learning model predictions and rasters of groundwater salinity in the Mississippi Alluvial Plain Data for Machine Learning Predictions of Nitrate in Shallow Groundwater in the Conterminous United States [Prospectivity Models] Prospectivity models - clastic-dominated (CD) and Mississippi Valley-type (MVT) GeoTIFF grids for the United States, Canada, and Australia National-Scale Geophysical, Geologic, and Mineral Resource Data and Grids for the United States, Canada, and Australia: Data in Support of the Tri-National Critical Minerals Mapping Initiative