Put another way, this is pretty similar to the interpolation approaches that would normally be used for datasets like this in the world of mineral exploration.

Put another way, this is pretty similar to the interpolation approaches that would normally be used for datasets like this in the world of mineral exploration. Kriging/co-kriging (i.e. gaussian processes) is the more commonly used approach in this particular field due to both the long history and the available hyperparameters for things like spatial aniostropy. However, kriging is really quite difficult to use with non-continuous inputs. RF is a lot more forgiving there. You don't need to develop a covariance model for discrete values (or a covariance model for how the different inputs relate, either).