Target-scale prospectivity modeling for gold mineralization within the Rajapalot Au-Co project area in northern Fennoscandian Shield, Finland. Part 2: Application of self-organizing maps and artificial neural networks for exploration targeting

Abstract

This paper is part of a two-publication series reporting target-scale prospectivity modeling results for gold enrichment in the Rajapalot project area. The study area is located in the Northern Fennoscandian Shield in Finland and consists of high-grade epigenetic-hydrothermal Au-Co prospects. The first publication of this series described the implementation of knowledge-driven- and hybrid- expert systems. In this second paper we demonstrate the application of machine learning methods such as unsupervised self-organizing maps (SOM) and K-means clustering and supervised artificial neural networks (ANNs). The results from SOM allowed the mapping of deposit-related geological patterns in the evidential layers. K-means clustering of the SOM results identified data clusters favorable for gold enrichment. Quantitative prospectivity values were computed using an ANN model trained on the SOM codebook vectors. This improved the resolution of the prospective-exploration areas mapped within the geospatial domains of the clusters, and reduced the exploration search areas. The ANN result has a significant area under curve value of 0.869 in the receiver operating characteristic plots, confirming the predictive ability of the model. Results identify new areas for exploration, as well as help prioritise drilling targets around the currently less explored prospects. In this second paper we therefore conclude that (1) SOM is an effective method for mapping and visualization of patterns (in the evidential layers) related to mineral deposits at the target scale, and (2) supervised classification of the SOM results, such as using an ANN, can help define new target areas for drilling around the existing prospects. Finally, this two-publication series collectively demonstrates that advanced machine-learning based pattern recognition and prospectivity modeling routines can be used for exploration targeting at camp- and mine- scales. The knowledge-driven methods are efficient at modeling mineralization processes and the data-driven methods are effective at identifying geological attributes related to mineral deposits and occurrences.