A GIS approach in Mineral Targeting with Narayanpet Kimberlite Spatial Dataset  Praveen Kumar Sinha Geologist (Jr.), AMSE Wing , Project INDIGEO, Training Institute, Geological Survey of India, Banglore E-mail : gsitihyd@hd2.dot.net.in  Dr. M. Surendra Nath Geologist (Sr.), Address:Project INDIGEO, Training Institute, Geological Survey of India,Bandlaguda,Hyderabad-500068 E-mail : gsitihyd@hd2.dot.net.in  Sikhendu De Geophysicsist (Sr.), AMSE Wing, Project INDIGEO, Training Institute, Geological Survey of India,Banglore Dr. P. K. Murlidharan Director (Geol.), Project INDIGEO, Training Institute, Geological Survey of India, Bandlaguda, Hyderabad-500068 E-mail : gsitihyd@hd2.dot.net.in  Ravi Shankar Misra Geophysicsist (Sr.), Project INDIGEO, Training Institute, Geological Survey of India, Bandlaguda, Hyderabad-500068 E-mail : gsitihyd@hd2.dot.net.in Abstract A cluster of kimberlites of ‘Narayanpet Kimberlite Field (NKF), Andhra Pradesh’ is located about 200 km north of the existing known ‘Wajrakarur Kimberlite Field (WKF)’ in East Dharwar Craton. This work, is an analysis in. an area of 2953 sq.km. falling in the NKF, towards mineral resources targeting where integration of evidence maps is attempted on Index Overlay Model in a vector GIS. Two groups of themes are identified in the digitised spatial dataset – one group of themes are those discrete geographic objects, which have played their role as causative features in the emplacement process of kimberlites and allied rocks in NKF. This includes database for lithology theme, database for faults and lineaments, database for gravity contours interpreted to high and low axes and database for ground magnetic contours interpreted to high and low axes. The other group representing the effect of the causes, is the database for kimberlite and allied rock bodies where 33 discrete known bodies are represented by points. The dataset was captured, populated with attribute values, assigned Polyconic projection with map-centre as origin of co-ordinate axes, edge-matched and mosaiced. A circumscribing ellipse around the kimberlite bodies is suggestive of a pattern of distribution. The major axis of this elliptical distribution suggests a probable E-W control on the kimberlite emplacement. But the ellipse has a considerably broad secondary axis perpendicular to the major axis, which suggests role of additional controls besides the above. Overplotting of the kimberlite occurrences on the generalised lithological domains (comprising Gneisses, Diapiric Granites and Deccan Basalt ) brings out a modified distribution pattern influenced by the litho-domains. Though the number of known kimberlite occurrences is almost equal in Gneiss and Granite (17 and 16 respectively), the controls on distribution pattern in these two lithological domains attributed to different sets of faults. The major axis of orientation of kimberlite bodies is E-W in Gneissic domain, whereas it is NNW-SSE in Granitic domain. Most of the theoretical concepts of kimberlite emplacement hold good for the NKF. The theoretical emplacement model for kimberlites emphasises - clustering in a form of ‘nest of crustal faults and fractures’ in proximity of a mega lineament. The mega lineament in NKF is traced from the Cuddapah basin to the east that extends hundreds of kilometres in E-W disposition. It is thus obvious that the linear geographic objects like fault-lineaments and gravity axes have controls on the pattern of distribution of kimberlites. The directional feature elements ‘as sets’ have different degree of controls, i.e., from control of major to secondary nature. So attribute for orientation is attached with a theme’s respective attribute table. Following the understanding of the cause and effect relations TRANSLATION of derived evidences on the input maps, sub-set output maps are made on major direction of orientation of faults and ‘domain specific’ gravity axes. Near tool – provides opportunity to define the closest-nearness in quantitative terms, between the causative line feature (for present study faults & lineaments and interpreted gravity axes) on a digital map and the point objects (kimberlites came to the real world as the effects of the causes in a particular set-up) in the other digital map. Using the summary statistical tool - mean for a specified case item (in present study fault_orientation for sub-sets of fault maps and gravity_orientation in granitic - and gneissic - domain sub-set maps) are acquired to establish the mean of the closest-distances between emplaced kimberlites and each causative sub-set line maps. For the present study, magnetic low and high map, sub-set maps for faults-lineaments and domain gravity axes are buffered (both sided, and round buffer) with the value of statistical mean for orientation class of their nearest kimberlites data extracted from the near operation. Before integration into a multi thematic map each class or sub-set of a thematic map was weighted, ranking on a suitability scale (0 – 9, maximum 10 weighted classes). The spatial association between known occurrence and the predictive datasets are used to workout weights, which were applied to predictive areas with similar characteristics to the known occurrence. On account of standardising the weights for classes or sub-sets of a theme or for inter-thematic maps factor (f) of posterior probability upon prior probability on the notion of Bayesian probability principle. Prior probability is the ratio of known kimberlites (33) upon area (NKF) and posterior probability is the ratio between circumscribed kimberlite bodies upon summed up area of the predictive zone (inside of the buffered zone). Assuming a common weight base (= 2) for the outside zone the weight (integer) for insides is subscribed by multiplying weight base and probability factor (f). Thematic integration of subsets was carried out followed by integration into a multi thematic map. Addition of weights and normalisation was carried out in a newly created field in attribute table. At decision making stage when kimberlite occurrence map is over-plotted on the integrated-multi-thematic-map in ARCMAP of ARCGIS Version 8.2, the merit of the model could be realised i.e., for each selection of a set of polygons qualifying a normalised weighted value or range of value one can have selection by location of the kimberlite bodies falling within concerned polygons. A degree of confidence can be expressed in terms of known kimberlites falling within area acquired by the polygons. So at a decision making stage, for each unique normalised weight classes the decision maker have both figures - statistically summed up area and improved confidence level. For example, polygons selected for high range of weights (8 to 9) make patches, having a total area of 80 sq.km., which contain 3 known kimberlite bodies in it. A higher ratios 3 nos./80 sq.km (target area) :: 33nos/2953 sq.km (project area) propose an improved degree of confidence by 3.38 times from prior probability. The model is for close observation in light of new discovery in the NKF in recent years. In light of the fact that kimberlites of NKF are in similar tectonic set-up with the similar age wise contemporaneous WKF (~1100 Ma, Radiometric dating of the bodies), the model has a scope to improve and widen its application throughout Dharwar Craton, which depicts heat flow regime of < 40 mw/ m2 (Heat flow map of India, after Ravi Shanker, 1988) that is favourable for Diamond stability. |