Geological mapping, the classification of bedrock into distinct identifiable units, has traditionally been conducted at the discretion of the field geologist on the basis of human-observable properties such as those of mineralogical composition and texture. In recent years technological developments have allowed the collection and analysis of ever more advanced quantitative geoscientific datasets. We are now approaching a point where migration of traditional mapping procedures to the digital domain is a feasible reality, with such benefits as consistency, transferability and transparency. One issue that we encounter is that the most geologically informative measurements, such as those of chemical composition, tend to have their sampling density limited by their high cost. Meanwhile, remote sensed data will tend towards extremely high sampling density, but may lack stand-alone geological significance. Nonparametric regression techniques have the potential to negate this issue by modelling the most geologically informative measurements as complex interactions of multiple remote sensed covariates. In this poster we present the use of random forest regression to model soil geochemistry in south west England using remote sensed data, and demonstrate how clustering of the predicted high resolution soil geochemistry is able to differentiate geological units – a process that can be trained to match pre-existing rock classifications.
We find that random forest regression based on remote sensed data is capable of predicting element concentrations in soils with superior accuracy to that of ordinary kriging of sparsely sampled point data. Crucially the random forest predictions incorporate the high resolution structure of the remote sensed covariates. This allows geological units, in this case defined purely on the basis of the geochemical composition of their soils, to be mapped with sharp boundaries limited only by the resolution of the remote sensed covariates. It seems likely that such techniques could take centre stage in the future of geological mapping: improving not only on the consistency of classified maps based on human observations, but also allowing the continuous mapping of any geologically constrained variables, such as radon potential, to the best resolution and accuracy that our covariate datasets can support
