About Mineral Prospectivity Modelling
What do you get out of a prospectivity model?
Prospectivity modelling produces a map showing those areas that are most likely to contain economic concentrations of the metal or mineral you're exploring for (e.g. the map on the right shows those areas most likely to host epithermal gold mineralisation in the Coromandel region, New Zealand). These types of maps can be used in GIS software to show where the most prospective areas are relative to tenements, existing mine sites, historical exploration, or processing facilities. The map produced from the modelling software is commonly called a predictive map or posterior probability map because it shows the statistical probability of the metal or mineral of interest occurring in a predetermined area. For statistical reasons geologists prefer to interpret the probabilities as a relative measure of favourability by ranking the data (e.g. high, moderate, low, or poor classifications in the example map for the Coromandel). This classified and ranked map can then be used by the explorer to target exploration in highly prospective areas and place lesser importance or even relinquish land that is not prospective. The spatial data modelling gives the explorer sound statistical information for financial and tenement management decision making.
How and why does the model work?
Geological, geochemical, and geophysical exploration data and spatial data modelling techniques are used to create predictive maps that represent aspects of a particular mineral system defined by the mineral system model for that system. Predictive maps are made up of two or more classes that will have either a positive or a negative association with the mineralisation. The probability of a deposit occurring in a particular class can be determined by expert opinion or by using a more objective statistically calculated value using the weights of evidence technique (see weights of evidence modelling in our predictive modelling section). For example, if most of the gold mines in a study area occur along SE trending faults and in granitic rocks, the weights of evidence probabilities for these variables would be much higher than those for NE trending faults in sandstone rocks. These are known as positive correlations and are predictors of the presence of mineralisation. The weights of evidence technique also calculates the negative correlation of a variable (caused by the absence of training data within a particular class) which provides important information on the prospectivity of an area. For example, if you know that gold mines in your study area never occur in marble or along folds you can exclude this land from additional data collection and reduce the cost of exploration significantly. The statistical combination of weighted predictive map layers produces the final prospectivity map.
When all the data variables have had probabilities assigned to them they are combined into one map (see illustration below) using the probabilities to weight the relative importance of the variables. From our example above, the areas of high prospectivity in the model are where SE trending faults and granites occur together, areas of lower prospectivity are where just one of the positive predictive variables occurred. Prospectivity values would be lower in areas that contained either of the negative predictive variables and the areas of lowest prospectivity would be where both negative predictive variables (marble and folds) were present. Our example here was simple as it only contains four predictive variables (granite, marble, faults, and folds). In reality nature is much more complex and dozens of predictive variables are used to create a prospectivity map.
Spatial data modelling is one of the best techniques to assess mineral prospectivity as it allows the combination of all the important predictive variables related to your mineral deposit model into one map. It is more powerful than just using single predictive variables such as rock chip geochemistry contour maps or geological maps. The model is also based on statistics, this means that it is not bias to previous ideas, current exploration trends, or even the campfire stories of old miners! It is based on what's been measured on the ground and which of these measurements are most related to your mineralisation model (both positive and negative correlations).
What goes into a prospectivity model?
Although the recipe for the formation of an ore body can be simplified to geology, geochemistry, and geophysics the combination of predictive maps that can be created from these base data are many and varied. The predictive maps are chosen either statistically by using the weights of evidence technique or by expert opinion. In either case the predictive maps have to have some relationship to the processes that formed the ore deposit in question. A variety of predictive variables (e.g. proximity to granite plutons, proximity to reactive rocks, NE trending faults) may be extracted from a single geological map.
Regional or country scale geological mapping and geophysical data sets are excellent data sources for modelling as they provide continuous data coverage, minimising problems associated with missing data. Point geochemical data are also valuable and need to be analysed for anomalous geochemical associations before they can be used in spatial data modelling. Most of this data is from historical exploration and is freely available from state and national Geological Surveys. The data is often available in a digital format ready for use in a GIS and spatial model.
What can you do after the modelling is done?
Prospectivity maps have many more uses than display and map production. You can use the probability data from the model to focus your exploration time and money on highly prospective areas, acquire or relinquish new tenements based on their prospectivity, or even use the modelling results to raise capital. Prospectivity modelling can also be used to manage your exploration programs.
The model allows you to work out the new and or more detailed data that needs to be collected over the prospective areas. The model can be rerun to assess the effectiveness of the new data in enhancing the prospectivity of the area being tested. For example, an explorer may identify from modelling of historical data a region of a likely gold deposit based on geological mapping, rock chip geochemistry and stream sediment geochemistry. They'll then raise funds from investors using the model to show them where and why there's likely to be gold. With this money they can go out and collect soil samples and geophysical data and re-run the model before deciding on areas to consider for their advanced field work or drilling programs.