Unsupervised clustering and empirical fuzzy memberships for mineral prospectivity modelling

We propose to increase the role of empirical methods in mineral prospectivity modelling for two reasons: 1) to make use of data more effectively and 2) to decrease the effect of subjectivity included in expert interpretation. We present two approaches for using known mineral occurrences to define the relationship between observed or measured geoscientific parameters and the occurrence of mineralizations. In the first approach, we define the fuzzy memberships of each geoscientific parameter separately for fuzzy logic modelling. Our approach proves to be highly useful for investigating the quality of the data in addition to defining the membership transformation functions. In our test case, the data are somewhat scattered due to the inherent variability of ore-forming environments, and manual evaluation was required to guide the computations. For the second approach, we present a technique for delineating non-prospective regions to be able to focus more detailed prospectivity modelling to potentially prospective regions. Our study not only highlights the advantages of using computational methods in prospectivity modelling, but also emphasizes the important role of geological expertise in the modelling process.