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Bayesian modeling of NURE airborne radiometric data for the conterminous United States: predictions and grids
This data release includes estimates of potassium (K), equivalent uranium (eU), and equivalent thorium (eTh) for the conterminous United States derived from the U.S. Geological Survey's (USGS) national airborne radiometric data compilation (Duval and others, 2005). Airborne gamma ray spectrometry (AGRS) measures the gamma-rays that are emitted from naturally occurring radioactive isotopes found in rocks and soil, the most abundant of which are potassium (K40), uranium (U238), and thorium (Th232). Radiometric data can aid in exploration of critical mineral resources, including deposits of barium, fluorine, titanium, beryllium, niobium, rare-earth elements, and uranium. There is also growing interest in using radiometric data to map soil properties.
The airborne radiometric data are an example of compositional data that are non-stationary (that is, the mean and the standard deviation vary spatially). It is therefore important to apply statistical techniques that account for both properties when gridding these data. To this end, a Bayesian hierarchical model coded in the Stan probabilistic programming language was used to estimate spatial variations of the mean and standard deviation in potassium (K), equivalent uranium (eU), and equivalent thorium (eTh) (Ellefsen and Van Gosen, 2020; Ellefsen and others, 2020). Using the national airborne radiometric data compilation, new grids for the conterminous United States were created for these three radioactive isotopes. Processing steps are described briefly in this data release. Users are advised to refer to the related USGS Techniques and Methods reports (Ellefsen and Van Gosen, 2020; Ellefsen and others, 2020) for a full description of the methods and processing steps.
Complete Metadata
| @id | http://datainventory.doi.gov/id/dataset/e0517c346656383db0b24d232d1387d9 |
|---|---|
| bureauCode |
[ "010:12" ] |
| identifier | USGS:5dd2f264e4b0695797628351 |
| spatial | -127.8549,23.165,-65.3742,51.5148 |
| theme |
[ "geospatial" ] |
