Since the samples were collected from different media and analysed by different laboratories over nearly 30 years, it was first necessary to assemble the "best picks" from each sub-population, and then to relevel the various surveys, for each element, to provide uniform blends. Estimates were also made of missing data over some small areas. As a result, syntheses of the various surveys are now available on uniform grids, over a common area, for as many as 42 of the elements. These can form the basis for systematic analysis.

As a start, we have applied various clustering methods to the 42 element data. The results show marked correlations with geology. This leads to the idea of using a neural network to model the geochemistry in areas where the geology is known, and then to apply this model to infer the bedrock geology in the non-outcropping areas. The resulting inferred geology, wherever geochemistry is known, is then almost identical to mapped geology in areas of outcrop, and blends well with mapped geology along the margins, where there is no geochemistry. These results show that geochemistry combined with neural networks can provide a powerful tool for mapping bedrock geology concealed by a veneer of glacial overburden in the QUEST project area.

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