Knowledge Driven GIS Modelling Techniques for Copper Prospectivity Mapping in Singhbhum Copper Belt – A Retrospection (III) Exploration Model and GIS Datasets: Keeping the above observations and genetic model into account, a conceptualized exploration model has been generated which is illustrated below (Anon, GSI, 1991):
In the knowledge driven approach, the model dissected the constituents for formation of mineral deposit into different genetic parts. These constituents are called as factors: those are possibly responsible for formation of mineral deposit. These factors are required because the physical and chemical principles governing the formation of mineral deposit are too complex for direct prediction from mathematically expressed theory. Thus, the prediction is mostly relying on the empirical relationship generated from deposit model (Bonham-Carter, 1994). It is also to be noted that presence of a suitable factor though increase the chance of finding the mineral deposit but absence of it not always ruled out the possibility. Once the conceptual model is conceived; the suitable factors are extracted from the geological, geophysical and geo-chemical dataset. These factors are overlain in GIS by different mathematical models to produce a single prospectivity map showing areas, which are conforming or jointly illustrating these factors or not. Areas, which satisfy all, or majority of the factors are turned to be the high suitable zones for further exploration. There are several GIS models available for combining the exploration dataset on identified spatial relationship between the GIS dataset and conceptual model. These models are applied on the spatial factors generated from the exploration dataset on Singhbhum Copper Belt and are illustrated below. a) Boolean Logic Method: In this model, all identified spatial factors which are responsible for copper mineralisation can only have two states; favourable (True or 1) and nonfavourable (False or 0). Thus, by this process a traditional conditional operator can be applied on the dataset; the operator can be ‘Boolean and’ or ‘Boolean or’; for classifying the multi-thematic maps to a single prospectivity map. In other word, the areas, which is positive to the above criteria, is taken as true and reverse is false. Thus, the final product is a classified map that illustrates two states i.e. prospective or non-prospective. In case of Singhbhum Copper Exploration data a criteria (Prescriptive Boolean model, Bonham-Carter, 1994) is chosen as [( favourable lithology = ‘metasediment + metavolcanics’) and ( within 1km of shear-zone) and (ground_geophysical_anomaly = ‘High_response’) and (airborne_magnetic = ‘moderately_magnetic’) and (geochemical_copper_anomaly_value > 200ppm)]: if this criteria is true then the area is prospective (value, 1) for copper mineralisation other wise not (value, 0) (Das et. al., 2002). The ‘and’ operator in the above statement is ‘Boolean and’. The GIS model is stated in Fig 1, stating two states, high probability and low probability, high probability is suitable for further detailed exploration. 
b) Index Overlay Method: In this method, the evidence (factors) 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 this map combination rule. 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 maps are receiving different weight (Bonham-carter, 1994) depending on the exploration model. An area that 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. After defining the score 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). |