Checking billy barr’s snow log…
Soils form a complex part of the environment, providing important functions in systems such as food production, groundwater movement, and ecosystem health. There have been numerous studies on soil geochemistry and the origin of elements within soils, but few studies into quantifying the relationships between elements and geographical data on a national scale. Using geochemical data obtained by the USGS, the surficial soils of the US were defined by a 13-endmember system using Polytopic Vector Analysis and mapped in GIS. Correlation matrices provided Pearson’s correlation coefficients for elements in the Top 5 cm soil profile, the A horizon, and the C horizon. The Top 5 cm element concentrations were also correlated with the endmembers, and geographical data for land cover, soil order, and soil pH. The quantification of these relationships supported many of the spatial patterns shown in maps and provided insight into other relationships, especially where correlation is weaker. The high spectral resolution of hyperspectral imagery has provided revolutionary advancements in many fields, including mineral identification. Hyperspectral core data from the company Corescan allowed for quantifiable mineral proportions for core sections from Karaha Telaga-Bodas, in Indonesia, and Lake City Caldera, in Colorado. The Alteration Strength Index (ASI) is a value based on mineral parameters that correlate to rock strength. The incorporation of hyperspectral data in the ASI aimed to improve the accuracy of the mineralogical parameters and provide more reliable strength data which in turn would reduce the need for time-consuming lab testing. Results showed that for the Telaga Bodas and Lake City samples, the ASI was not able to provide rock strength estimates within a reasonable range, and calibration of mineralogical parameters may be needed for samples with low ASI values. Though hyperspectral data may be a valuable tool in this venture, other issues with the current ASI would need to be resolved.
Items connected by shared entities, co-authorship, citations, or semantic similarity.