Mineral potential map by a knowledge driven GIS modelling: An example from Singhbhum copper belt, Jharkhand Alteration evidences The main litho-units which underwent alteration as biotitisation, chloritisation, tourmalinisation and sericitisation were selected from the geological maps and digitised. Polygon coverages were prepared from them with an added attribute. Geophysical evidences
Interpreted geochemical anomalies (from analytical value of bed rock samples) in the form of contour maps available were digitised into polygon coverages. GIS modelling, Weight on evidence in Index overlay method Weight of evidence is the method of combining subjective evidences on the basis of their bearing towards a process quantitatively. The method was originally developed for non-spatial application in medical science and adopted in the late 1980’s for mineral potential mapping. The evidence consists of a set of exploration dataset (maps) and weights are estimated from the measured association between known mineral occurrences and the exploration model for a particular terrain. The hypothesis then repeatedly evaluate all possible location of the maps using the weight and in turn produce a mineral potential map in which the evidences of several map layers are combined by a map combination rule, say index overlay method. Thus, this method belongs to those groups, where multicriteria analytical procedure is employed to generate a decision making map. In the index overlay method, each input map (layer of evidence) to be used as evidence is assigned a different score (weight), as well as the map themselves are receiving different weight (Bonham-carter, 1994) depending on the exploration model. The weight can be calculated statistically depending on the number of input mineral occurrences and its relationship with a particular element or theme on a map. Otherwise, weight can be calculated on the basis of relative importance of elements/themes on the input and evidence maps by mineral deposit experts (as expected in knowledge driven approach). Assignment of weight or score to maps by statistical process may be applicable where a unified and definitive exploration model is unknown or the relationship / relative importance between different elements is uncertain. In contrast, an area which is geologically well explored with a relatively well-understood exploration model in hand (the present study area), assignment of weight on different themes or maps ought to be through knowledge driven approach. This not only help in developing a clear understanding of relationship between datasets (both geological, geophysical or geochemical ) but this also give flexibility to an exploration geologist to manipulate weight on different elements or evidence maps through geological knowledge about the terrain in different stages of analysis. This is advantageous for developing perhaps a variety of scenarios for different weight schemes, reflecting differences in opinion amongst experts, and allows the evaluation of sensitivity of the mineral potential maps to such differences. This flavour of understanding is generally absent in the data driven statistical approach where the system (the statistical model) directs the exploration and generates decision, which may be irrelevant in the complex geological system. After defining the score by statistical (data driven) or by knowledge driven approach for elements or maps, the average score ( index weight) is then defined by  Where S is the weighted score for an area object, Wi is the weight for the i-th input map, and Sij is the score for the j-th class of the i-th map, the value of j depending on the class actually occurring at the current location (Bonham-carter, 1994). In the present analysis, a knowledge driven intuitive approach was employed by seeking views from exploration geologist or exploration model for finalisation of the weight or score on different themes / evidence maps and finally all these evidence maps were combined by additive union method to generate the mineral potential maps. Probabilistic analysis to assign score was not possible due to, i) very complex evolutionary history of the terrain ii) data density and spacing is not congenial for carrying out statistical analysis. Thus an intuitive approach with inputs from exploration experts of the region was applied. Weights of evidence were calculated for each of the datasets analysed. Simple point in polygon analysis was performed in case of polygon domains (i.e., lithologic units, geochemical anomaly). In case of linear features, such as lineament, shear-zone buffer analysis was done to create geologically meaningful zones of influence. These zones were then analysed as polygons and weights computed. The final predictive map was calculated by overlaying the images (raster data) created from the weights of evidence, representing the deposit recognition criteria, and each map summed up the weights at every location. |