Mineral potential map by a knowledge driven GIS modelling: An example from Singhbhum copper belt, Jharkhand  Basab Mukhopadhyay  Niladri Hazra  Sujit Ranjan Sengupta  *Swapan Kumar Das Geological Survey of India, Geodata and Database Division CHQ, 27, J. L. Nehru Road, Kolkata gsi_chq@vsnl.com *Geological Survey of India, Project Geoinformatics ERO, MSO Building, Salt Lake City, Kolkata Abstract Mineral resource potential mapping is a complex analytical process, which requires consideration and integration of a number of spatial evidences like geological, geomorphological, wall rock alteration, etc., using the capability of analytical tools of Geographic Information System (GIS). The Singhbhum Copper Belt - a narrow, arcuate highly sheared linear zone in the Singhbhum Precambrian Terrain in Jharkhand, runs over a length of 128 km from Kharswan-Duarpuram in the west to Baharagora in the east. This modelling is an attempt to re-examine the huge inventory of spatial as well as attribute data in GIS datasets (collated from GSI published and unpublished work) in the light of certain evidences recognizable on a regional scale – the existing exploration model, favourable for copper mineralisation. The input data for the analysis include i) lithological evidences in the form of lithology, favourable contacts, ii) alteration evidences such as chloritisation/biotitisation/tourmalinisation /sericitisation etc., iii) geophysical anomalies such as aero-magnetic, radiometry, ground geophysics (SP, IP, EM), iv) structural evidences such as lineament, shear-zone and v) geochemical anomalies such as analytical value (copper) for bed rock samples. A knowledge driven weight on evidence approach was employed to establish relationship between the input datasets and exploration model. In this approach, individual basic layers of evidences are integrated in maps on the basis of a score assigned, according to their influence towards mineralisation. Each element of the input layer used as evidence is assigned a different score (weight) to generate secondary factor maps. In the next phase, the factor maps were combined with different map weight depending on their relevance towards mineralisation. Finally, all the factor maps were integrated to generate a mineral potential map by additive union, using index overlay method. The resulting mineral potential map in probability scale was cross validated by plotting the known mineral deposits, the model shows good match. It further identifies two localities, E and SE of Kanyaluka – Gohala area and a large area West of Turamdih. In between these two areas there are small lenticular pockets considered to be the potential site for further copper exploration. Introduction The Singhbhum Copper Belt - an arcuate linear zone in the Precambrian Singhbhum Crustal Province (PSCP), houses over 250 million tonnes of copper ore with variable tenor ranging from 0.5 to 4% of copper. Though the geological environment for the entire belt extending over 128 km from Kharswan-Duarpuram in the West to Baharagora in the southeast, is a favourable locale for sulphide mineralisation, economic deposits are restricted only over 15% of the total stretch in the central part of the belt. Many of these identified deposits lack the requisite tonnage necessary for mining. In view of the exhaustion of identified deposits for detailed exploration, ore-reserves have remained stagnant over a decade, an examination of the belt applying advanced GIS techniques seems worthwhile to find new locales for mineralisation. Mineral resource potential mapping is a very complex analytical procedure which requires simultaneous consideration of a number of spatial evidences - geological, geomorphological, structural, geochemical, geophysical etc. The capability of Geographic Information System (GIS) to manipulate such classified spatial information through amalgamated layers, makes it a unique tool for delineating potential locales. Flexibility of experimenting with spatial data followed by visualisation of its effect immediately, gives GIS a cutting edge over other contemporary techniques, for modelling mineral deposits. The PSCP has attracted a number of geoscientist through the ages. Five decades of exploration, has generated huge inventory of spatial and attribute data. This paper is an attempt to re-examine the data in the light of certain criteria favourable for mineralisation of copper, which are recognizable on regional scale. The predictive GIS model is based on weights of evidence analysis of lithological, structural, geochemical and geophysical data sets employing knowledge driven GIS approach. 
Page 1 of 7
| Next | |