A GIS approach in Mineral Targeting with Narayanpet Kimberlite Spatial Dataset The dataset was captured, populated with attribute values, assigned Polyconic projection with map-centre of the mosaic of four 1:50,000 topobases as origin of co-ordinate axes, edge-matched and mosaiced, thereby, all the themes were brought to a common reference system. This fascilitated overlaying and over plotting of spatial databases for GIS analysis. Study of over-plotted digital maps is helpful in revealing the pattern of occurrence, field controls on known bodies and theoretical understanding of emplacement model for known bodies. Visual analysis in understanding the pattern of distribution of kimberlites in NKF aided in understanding the controls on kimberlite emplacement. Small outcrops of geographic object like kimberlite - represented by points on the digital map may have three fundamental distribution patterns i.e., a) Complete spatial randomness, b) Clustered pattern and c) Regular pattern. The kimberlites of WKF and NKF represent a clustered pattern. Each cluster has limited aerial spread and the spread of kimberlites of NKF is in the form of an ellipse. On the whole, the ellipse precisely defines the kimberlite points. 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 E-W control.  Fig.3 : An ellipse define overall distribution of kimberlites of NKF  Fig.4 : Lithological domains and distribution of kimberlites of NKF Over-plotting 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 is attributed to different sets of faults. The major axis of orientation of the kimberlite distribution 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 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 faults, lineaments and gravity axes have controls on the pattern of distribution of kimberlites. The directional sets of feature-elements have different degree of controls, i.e., from significant to least significant. Derivation of theme based evidence maps: GIS provides unlimited opportunities to make observations on over-plotted thematic maps, delineation of predictive features and understanding their role in identification of prognostication zones. Following the understanding of the cause and effect relationships, the second phase in an analysis process, is to manipulate the data so as to derive theme base evidence maps. Table 1 gives the details of the need for manipulation, the analysis tool applied in spatial data manipulation and its objective as a part of prognostication over the Narayanpet dataset: |