Science Blog: GIS-based mineral potential targeting is making the most out of geoscience data to enable new discoveries

Vesa Nykänen, Research Professor

Mineral potential targeting or mineral prospectivity mapping can be defined as a multi-step process of extracting and weighting mappable features indicating the critical parameters of the mineral deposit types, or more preferably the mineral systems, that are being sought. This is achieved by combining the evidence layers, and finally ranking promising target areas for further exploration (Figure 1). The aim is to delineate the most favorable areas for the deposit type in question by using the data-analysis power of modern geographical information systems (GIS).

It all starts from understanding the origin of ore deposit types as a part of geological processes, and translating this knowledge into map patterns that can be used as proxies for these processes. In general terms, a mineral system consists of the source of the metals, pathways carrying metal-bearing fluids, and finally a chemical or physical trap that actually dictates the location of the mineral occurrence. Given that all the circumstances are beneficial, the occurrence may turn out to be an ore deposit with economic value and eventually become a mine.

To conduct mineral prospectivity mapping, we need digital maps and a platform that can be used to integrate these maps into a single map output in an organized way. GIS provides this platform, and a large variety of techniques are available to perform this task due to the efforts of researchers during the past decades. The most common division of the techniques divides them into data driven or knowledge driven according to the approach. Data-driven techniques can be either supervised or unsupervised, depending on whether prior knowledge of the modeled deposit type is used. A knowledge-driven approach translates expert understanding of the exploration criteria into a mathematical formula that is supposed to mimic the decision-making process of an exploration team. Regardless of the technique used, the output is a single prospectivity map that delineates the most favorable areas for mineral exploration from the less favorable areas. It seems to be popular to compare different techniques with each other and claim one to be superior over the others. However, the various techniques constantly produce fairly similar results, despite the great variability in their underlying mathematics. The controlling factor really appears to be the pre-processing of the input data together with the initial definition of the critical parameters derived from the mineral system model.

Model validation is a crucial part of prospectivity mapping, and various cross-validation techniques are available. Validation can be carried out by using the location of the known mineral deposits that were not used for training the model, or the model can be run several times by leaving out a proportion of the training sites and then comparing the results. One suggested method is the so-called receiver operating characteristics (ROC), which uses both true positive sites and true negative sites for validation. The true positive sites are easy to choose if known mineral deposits are present within the study area. However, the choice of true negative sites is challenging. One could select the other deposit types or drilling sites indicating that they did not hit the deposit. But what if the element in question was not assayed? One solution to this problem is to use random locations as true negative sites, and this has proven to be an appropriate way to validate the spatial models of mineral prospectivity.

So where are we heading with these methods? Definitely the new frontier is below the surface of the Earth. This is where the mineral deposits are, anyhow. The greatest challenge is the data. To be able to conduct mineral prospectivity mapping in 3D, we need appropriate data (Figure 2). Adding a new dimension also increases the uncertainty of the model a great deal, but challenges are meant to be tackled. This one can be solved by continuing to invest in world-class geoscience data acquisition in this country by using both public and private contributions, and by making these data, together with analysis tools, available for all players in the mining industry through the national geodata center, GTK.

Figure 1. Mineral prospectivity modeling flow chart.


Figure 2. Prospectivity modeling below the surface also requires real 3D data. This is a paleostress model based on the geological interpretation of a reflection seismic survey from Kolari, northern Finland. Courtesy of Tero Niiranen.


Yousefi M. & Nykänen V. 2017. Introduction to the special issue: GIS-based mineral potential targeting. Journal of African Earth Sciences 12: 1-4. https://doi.org/10.1016/j.jafrearsci.2017.02.023

Nykänen, V., Niiranen, T., Molnár F., Lahti, I., Korhonen, K., Cook, N. & Skyttä, P. 2017. Optimizing a Knowledge-driven Prospectivity Model for Gold Deposits Within Peräpohja Belt, Northern Finland. Natural Resources Research 26: 571-584. https://doi.org/10.1007/s11053-016-9321-4




Vesa Nykänen

Text: Vesa Nykänen

Dr Vesa Nykänen is a Research Professor in Geoinformatics, specializing in spatial data analysis, spatial data mining, and geological modeling. He has been at the Geological Survey of Finland (GTK) since 1998, using GIS as a tool for bedrock mapping and mineral exploration. He is currently focusing on the further utilization of GIS in geoscience applications, and is responsible for scientific and competence development in geoinformatics at GTK.