Multiple influences on regolith characteristics from continental-scale geophysical and mineralogical remote sensing data using Self-Organizing Maps

We investigate the characteristics of regolith through the application of statistical learning to diverse layers of terrestrial, continental-scale remote sensing data. This combination allows us to explore the multiple influences of bedrock, climate, biota, landscape and time on regolith development and properties: an interdisciplinary geoscience modeling problem. From a wide variety of available data for Australia, we select remotely sensed geophysical, geomorphological and mineralogical inputs with good spatial coverage. We use Self-Organizing Maps (SOM), a topologically constrained unsupervised statistical learning algorithm, to characterize the geophysical and mineralogical signatures of regolith and bedrock. Regolith materials cover more than 80% of the Australian continent, range in age from Precambrian to Quaternary and vary in thickness from less than a meter to more than a kilometer. The diversity of regolith cover type and character across Australia provides an opportunity to demonstrate knowledge discovery from remote sensing data. The outputs of our SOM analysis are combined with ground observations from locations showing naturally occurring anomalous concentrations of nickel, tin and uranium. We identify a minimum number of natural clusters indicating subtle but significant differences in regolith and bedrock mineralization characteristics. Our results show that SOM identifies spatially contiguous regions representing unique regolith and bedrock materials. In the Yilgarn Craton we observe key differences in landscape character, density of the crust, and relative abundance of radioactive elements and alumino-silicate and ferric oxide minerals. These properties discriminate between nickel-prospective residual deeply weathered regolith formed on mafic and/or ultramafic bedrock and uranium-prospective Cainozoic paleochannels containing felsic bedrock source materials. National-scale data are publicly available for many continental regions, as in the Australian example, and our approach has general applicability. We demonstrate that remote sensing data may be used to understand the regolith, revealing the interplay between environmental history and bedrock character at regional scales, and differences between residual and transported regolith, provenance of source materials and their relative ages.