Cracknell, et al., report that the results of a pilot study into the application of an unsupervised clustering approach to the analysis of catchment-based National Geochemical Survey of Australia (NGSA) geochemical data combined with geophysical and geological data across northern Australia are documented. NGSA Mobile Metal Ion® (MMI) element concentrations and first and second order statistical summaries across catchments of geophysical data and geological data are integrated and analysed using
Self-Organizing Maps (SOM). Input features that contribute significantly to the separation of catchment clusters are objectively identified and assessed.
A case study of the application of SOM for assessing the spatial relationships between Au mines and mineral occurrences in catchment clusters is presented. Catchments with high mean Au code-vector concentrations are found downstream of areas known to host Au mineralization. This knowledge is used to identify upstream catchments exhibiting geophysical and geological features that indicate likely Au mineralization. The approach documented here suggests that catchment-based geochemical data and summaries of geophysical and geological data can be combined to highlight areas that potentially host previously unrecognized Au mineralization.
The identification of a high percentage of catchments with high mean Au code-vector ratios being located downstream from Au mines and mineral occurrences is a significant result as it suggests Au is being liberated from areas of Au mineralization and transported downstream potentially both mechanically and hydromorphically.
Au is subsequently detected by MMI extraction in sediments at or below the break in slope where hydrological energy decreases (mechanical transport pathway) and aquifers potentially intersect the land surface (hydromorphic transport pathway). This information has been used to define catchments upstream of those with high mean Au
MMI code-vector ratios in outlet sediments as potential hosts of Au mineralization. Three upstream catchment clusters that have a close spatial relationship with high mean Au code-vector ratios clusters are identified. These three clusters intersect a high frequency of Au mines in areas that contain a mixture of felsic intrusive, sedimentary and
medium-grade metamorphic rocks. The geophysical characteristics of these prospective catchment clusters, and others with high proportions of metamorphic rocks, indicate high contrast in either magnetic and gravity signals at wavelengths of less than 10 km, or elevated and highly variable total count radiometrics signals.
Further investigation into the role that geochemical element mobility plays in governing the relative contribution of catchment characteristics is required. In addition, understanding the significance of the spatial frequency characteristics of geophysical data is needed to clarify the relationship between code-vector ratios and mineralization.
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