## 5 Discussion  
The aim of this study was to ascertain if geochemical parameters could be predicted from spectral measurements. The spectral measurements in this study have a 1-to-1 correspondence with whole-rock geochemistry and provide a data rich avenue for investigating the feasibility. The global findings demonstrated that a relatively small amount, compared to the total number of samples in the entire dataset, of spectral samples and matched geochemistry can be used to successfully train a combined NMF and RFR model to make accurate and quantitative predictions. The dataset used for this study are from a reasonably uniform and non-diverse geology and allowed accurate predictions to be made that extended far beyond the spatial confines of the training and test dataset locations. If the underlying geology was to markedly depart from that of the training and test data used to build the models, then it would require new models to be built that can incorporate such changes. However, not all the whole-rock entries were modeled well or even able to be modeled. To successfully predict quantitative values for a given element requires that the element in question has a broad range. By this it is meant that if an element, such as P or S, are not well represented within the geology and samples then there is, as intuitively expected, nothing to model. In this study several geochemical elements had a small range of values  
Figure 6: The measured and predicted downhole values for Fe, SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\) and LOI of four randomly selected drillcore where the length of the drillcore comprised greater than 150 entries. The other whole-rock geochemistry parameters are not shown due to the values being comprised of background only which only appear as singular valued traces along y = 0. Overall a good correspondence between the predicted and measured values is observed with generally minor deviations noted. and/or where distributed close to the origin. These represented background values of which larger values were not always present, as was the case with TiO\({}_{2}\) and K\({}_{2}\)O, and for which the predictive power of the model with these elements was limited. Other elements such as CaO and MgO demonstrated bimodal distributions whose range, even though the bulk of the values are scattered accumulated about the origin, allowed a reasonable quantitative prediction to be made. In most cases the RMSE on those elements which were range restricted were still small and can be used confirm that the element is of background quantities. This aspect was reiterated by producing per-drillcore R\({}^{2}\) scores and RMSE values. Those elements that had an extensive range, such as Fe and SiO\({}_{2}\), produced per-hole high R\({}^{2}\) scores and small RMSE values. On the contrary, and specifically for those elements that were range constrained, poor R\({}^{2}\) scores (while still producing low RMSE in most cases) result. However, when drillcore are encountered with an extended range the individual R\({}^{2}\) increases and the RMSE also slightly increase. While this may seem an obvious result it is worth noting and bearing in mind that if one trains and validates a model and applies it, for example to a single drillcore, the returned result, if it was compared later to measured values for that same single drillcore, may appear to be poor. In other words, producing an R\({}^{2}\) score on the singular drillcore should not be used as an indicator of model performance. Additionally, the use of a RFR model in this case has proven to be successful but it has limitations that may necessitate the retraining of the model at future dates. While the RFR is robust it does not extrapolate and hence cannot return predictions beyond the largest and smallest values used to train the model. Thus, if the initial data used to train the model is a subset of a greater range, then the model would need retraining to account for the extended range.