Authors / CoAuthors
Abstract
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 remote sensing, 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 covers more than 80% of the Australian continent, ranges in age from Precambrian to Quaternary and varies in thickness from a few meters to more than a kilometer. The diversity of preserved regolith across Australia therefore provides a rich demonstration example of knowledge discovery from remote sensing data in the case of diverse inputs and complex interactions. 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 groups, or 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 in situ 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 wide general applicability. We hence demonstrate that remote sensing data may be used to understand the multiple influences on regolith, revealing the interplay between environmental history and bedrock character at regional scales, and differences between in situ and transported regolith, provenance of source materials and their relative ages.
Product Type
nonGeographicDataset
eCat Id
82525
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Cnr Jerrabomberra Ave and Hindmarsh Dr GPO Box 378
Canberra
ACT
2601
Australia
Keywords
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- External Publication
- Australian and New Zealand Standard Research Classification (ANZSRC)
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- Earth Sciences
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- Published_Internal
Publication Date
2014-11-26T00:00:00
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geoscientificInformation
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[-44.0, -10.0, 112.0, 154.0]
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