Update of North American Airborne Radiometric Survey Data Using Advanced Statistical Techniques and Parallel Computing
The purpose of this project is to update the current gridded products created from the North American airborne gamma ray spectrometry data using new statistical techniques to analyze spatial data and to create higher quality national airborne radiometric grids.
Science Issue and Relevance
Airborne gamma ray spectrometry (AGRS) measures the gamma-rays that are emitted from naturally occurring radioactive isotopes that are found in rocks and soil, the most abundant of which are potassium (K40), uranium (U238), and thorium (Th232). In the 1970s, airborne radiometric surveys were flown over much of the conterminous U.S. and Alaska as part of the National Uranium Resource Evaluation (NURE) as well as parts of Canada under the Uranium Reconnaissance Program. USGS has developed new statistical techniques for analyzing spatial data that are non-stationary (that is, the mean and the variance are not constant) and comprised of a large number of measurements. The new statistical modeling is applicable for multiple types of earth science data, which could include concentrations of potassium, uranium, and thorium. The method provides more geologic information that can help geologists understand the geologic processes that create mineral deposits.
Application of new advanced statistical methods to existing airborne gamma ray spectrometry data will yield grids that are scaled appropriately for the data and quantify uncertainty of the gridded values. Metadata published with the new grids will describe the processing steps in detail to help users understand the data so users can analyze it appropriately. Detailed metadata will also help maintain the longevity of the U.S. (and Canadian) datasets and demonstrate their value. By testing the newly gridded data with case studies in areas where relationships between AGRS data and mineral deposits (or other environmental data) are well documented, this project will also assess the quality of the new grids and document potential need for flying new airborne radiometric surveys.
Methodology to Address Issue
- Adapt statistical methods developed by USGS for application at the national scale.
- Create raster surfaces of compositional mean and variance for potassium, uranium, and thorium at a scale appropriate to the quality of the data.
- Demonstrate with case studies how the newly processed airborne radiometric grids compare with field data.
Return to Mineral Resources Program | Geology, Geophysics, and Geochemistry Science Center
Below are data or web applications associated with this project.
Bayesian modeling of NURE airborne radiometric data for the conterminous United States: predictions and grids
Below are publications associated with this project.
Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences
User guide to the bayesian modeling of non-stationary, univariate, spatial data using R language package BMNUS
The purpose of this project is to update the current gridded products created from the North American airborne gamma ray spectrometry data using new statistical techniques to analyze spatial data and to create higher quality national airborne radiometric grids.
Science Issue and Relevance
Airborne gamma ray spectrometry (AGRS) measures the gamma-rays that are emitted from naturally occurring radioactive isotopes that are found in rocks and soil, the most abundant of which are potassium (K40), uranium (U238), and thorium (Th232). In the 1970s, airborne radiometric surveys were flown over much of the conterminous U.S. and Alaska as part of the National Uranium Resource Evaluation (NURE) as well as parts of Canada under the Uranium Reconnaissance Program. USGS has developed new statistical techniques for analyzing spatial data that are non-stationary (that is, the mean and the variance are not constant) and comprised of a large number of measurements. The new statistical modeling is applicable for multiple types of earth science data, which could include concentrations of potassium, uranium, and thorium. The method provides more geologic information that can help geologists understand the geologic processes that create mineral deposits.
Application of new advanced statistical methods to existing airborne gamma ray spectrometry data will yield grids that are scaled appropriately for the data and quantify uncertainty of the gridded values. Metadata published with the new grids will describe the processing steps in detail to help users understand the data so users can analyze it appropriately. Detailed metadata will also help maintain the longevity of the U.S. (and Canadian) datasets and demonstrate their value. By testing the newly gridded data with case studies in areas where relationships between AGRS data and mineral deposits (or other environmental data) are well documented, this project will also assess the quality of the new grids and document potential need for flying new airborne radiometric surveys.
Methodology to Address Issue
- Adapt statistical methods developed by USGS for application at the national scale.
- Create raster surfaces of compositional mean and variance for potassium, uranium, and thorium at a scale appropriate to the quality of the data.
- Demonstrate with case studies how the newly processed airborne radiometric grids compare with field data.
Return to Mineral Resources Program | Geology, Geophysics, and Geochemistry Science Center
Below are data or web applications associated with this project.
Bayesian modeling of NURE airborne radiometric data for the conterminous United States: predictions and grids
Below are publications associated with this project.