Predictive Geological Mapping from Geophysical Data Using Self-Organizing Maps: A Case Study from Baie Verte, NL, Canada

Self-organizing maps (SOMs) are a type of unsupervised artificial neural networks clustering tool. SOMs are used to cluster large multi-variate datasets. They can identify patterns and trends in the geophysical maps of an area and generate proxy geology maps, known as remote predictive mapping. We applied SOMs to magnetic, radiometric and gravity datasets compiled from multiple modern and legacy data sources over the Baie Verte Peninsula, Newfoundland, Canada. The regional and local geological maps available for this area and the knowledge from numerous geological studies allowed for assessing the accuracy of the SOM-based predictive mapping. Proxy geology maps generated by primary clustering directly from the SOMs and secondary clustering using a k-means approach reproduced many geological units identified by previous traditional geological mapping. Of the combinations of datasets tested, the combination of magnetic data, primary radiometric data and their ratios, and Bouguer gravity data gave the best results. We found that using reduced-to-the-pole residual intensity or analytic signal as the magnetic data were equally useful. The SOM process was unaffected by gaps in the coverage of some of the datasets. The SOM results could be used as input into k-means clustering as k-means clustering requires no gaps in the data. The subsequent k-means clustering resulted in more meaningful proxy geology maps than were created by the SOM alone. In regions where the geology is poorly known, these proxy maps can be useful in targeting where traditional, on-the-ground geological mapping would be most beneficial which can be especially useful in parts of the world where access is difficult and expensive.