Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite, Nunavut, Canada

This paper outlines the process taken to create two separate gold prospectivity maps. The first was created using a combination of several knowledge-driven (KD) techniques. The second was created using a relatively new classification method called random forests (RF). The purpose of this study was to examine the results of the RF technique and to compare the results to that of the KD model. The datasets used for the creation of evidence maps for the gold prospectivity mapping include a comprehensive lake sediment geochemical dataset, interpreted geological structures (form lines), mapped and interpreted faults, lithology, topographic features (lakes), and known Au occurrences. The RF method performed well in that the gold prospectivity map created was a better predictor of the known Au occurrences than the KD gold prospectivity map. This was further validated by a fivefold repetition using a subset of the input training areas. Several advantages to the use of RF include (1) the ability to take both continuous and/or categorical data as variable inputs, (2) an internal, unbiased estimation of the mapping error (out-of-bag error) removing the need for a cross-validation of the final outputs to determine accuracy, and (3) the estimation of importance of each input variable. Efficiency of prediction curves illustrates that the RF method performs better than the KD method. The success rate is significantly higher for the RF method than for the KD method.