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Knowledge Driven GIS Modelling Techniques for Copper Prospectivity Mapping in Singhbhum Copper Belt – A Retrospection
All the evidences or spatial relationship are treated equally and quantified as continuous surface defined by fuzzy prospectivity value. Initially, every data point in each input layer of evidence (comprising of lithology, favourable contacts, aeromagnetic,shear zone, lineaments and faults, ground geophysical, wall rock alteration and geochemical anomaly) is represented by vector of unit length i.e. confidence as unity for each point. The vectorial fuzzy combination employing equation 1 & 2, combines the above primary layer of evidences & intermediate evidence maps and generate two predictive maps, one representing the copper prospectivity map of the area and the other representing the measure of confidence in defining the prospectivity (fig. 4). The ideal combination of finding the locales of copper deposit will be those places where both confidence and prospectivity is high. Such a map is generated for this terrain by deploying a simple logical operator stated in fig. 4. This model (fig.4) is cross validated by plotting the known copper deposit/occurrences of this terrain, which fits well. The marked area defined by P in fig.4, can be treated as potential sites for further copper exploration (Mukhopadhyay, in press).
(V) Discussion and Conclusion:
The methods of combining multiple factorial maps in a knowledge driven GIS modeling encompass a wide variety of models. In the mineral exploration, map combination process is based on empirical to theoretical principles which is responsible for ore deposit formation in that tectono-stratigraphic domain. similarly, such principle varies from terrain to terrain and for different metallogenetic domains. Four basic map combination methodologies namely Boolean logic, Index overlay, fuzzy inference modeling and vector fuzzy modeling are applied on the causative factors generated from exploration dataset of Singhbhum Copper Belt on the basis of exploration model. The inferences that are drawn on the basis of these GIS models are summarized below:
- The Boolean prescriptive model suggests a couple of locales west of Turamdih as the potential site for further exploration (Fig 1).
- The GIS model generated by Index Overlay Method (Fig 2) unequivocally confirmed that the copper mineralization is magmatic and shear controlled which is the existing view regarding copper mineralization in Singhbhum. The model also identifies two broad localities; one E and SE of Kanyaluka area and a large area west of Turamdih.
- The GIS model created by the fuzzy operators confirms coincidence between bed rock copper anomaly and high suitability zone predicted by the model (Fig 3). The final map (Fig 3) inferred two clusters (marked by P) located west of Turamdih is highly suitable for further copper exploration.
- The predictive GIS model generated by vector fuzzy logic methodology created two maps; one regarding the prospectivity of copper and other representing the confidence regarding defining the prospectivity. The final GIS model (Fig 4) defined areas where both confidence and prospectivity is high. This model delineates couple of locales west of Turamdih as prospective sites for further exploration.
Now, from the above discussion it can be safely concluded that the product/result of the above four combination methodologies pointed to a more or less same place i.e. west of Turamdih as potential site for further exploration. It is also true that for all the above methodologies the exploration dataset and the genetic/exploration model remains the same. This is because the assignment of relative importance of layers in terms of quantified scoring and rule of map integration in a knowledge driven approach is strictly dependent on the exploration model, which remains the same for all the GIS models described earlier. In conclusion, this can be said that irrespective of subjectivity embedded in the methodology, the product of knowledge driven GIS analysis is dependent on the dataset, its spatial relationship and genetic/exploration model of the area. This is true for this terrain but for other tectono-stratigraphic domain it demands testing and consideration.
Acknowledgement:
The authors expressed their gratitude to all the officers worked on Project Singhbhum and Project Geoinformatics – Singhbhum Precambrian on 73J. They also remains grateful to Shri E. V. R. Parthasaradhi, Dy. D. G. (IT), Dr. M.K. Mukhopadhyay and Dr. J. Simhachalam, Director, Geodata and Database Division for encouragement and constructive criticism, and finally to Director General, GSI for giving permission to publish this paper.
- An,P., Moon, W.M. and Rencz, A., 1991. Application of fuzzy set theory for integration of geological, geophysical and remote sensing data, Canadian Journal of Exploration Geophysics, V 27, pp 1-11
- Anon, GSI, ER, 1991. Unpublished GSI report on Project Singhbhum – Synthesis of data of Singhbhum Copper Belt, Singhbhum District, Bihar: Part I & II., unpublished.
- Banerji, A. K., 1962, Cross folding, migmatisation and ore localization along part of Singhbhum Shear Zone, Bihara, Economic Geology, Vol 57, pp 50-71.
- Banerjee, A. K., 1981, Ore genesis and its relationship to volcanism, tectonism, granitic activity and metasomatism along Singhbhum Shear Zone, Eastern India, Economic Geology, Vol 76, pp 905-912.
- Bonham-Carter, G. F.,1994, Geographic Information System for Geoscientists: Modelling with GIS, Pergamon.
- Carranza ,E.J.M.. and Hale, M., 2001, Geologically-contrained fuzzy mapping of gold mineralisation potential, Baguio district, Phillipines, Natural Resource Research, V10, no 2 , pp 125-136.
- Changkakoti, A.,Gray, A., Morton, R. D. and Sarkar, S. N., 1987, The Mosabani Copper Deposit, India – A preliminary study on the Nature and genesis of ore fluids, Economic Geology, Vol 82, pp 1619-1625.
- Das, S.K., Mukhopadhyay, B., Laskar, T. and Roy, B.C., 2002, Abstract volume of National seminar on Role of Information Technology in Geosciences, February 19&20, 2002, Bhubaneshwar, pp 88-89.
- Dunn, J. A., 1937. The mineral deposits of Eastern Singhbhum and sorrounding areas. Memoir Geological Survey of India, Vol 69, Part I.
- Mukhopadhyay, Basab., Hazra, Niladri.,Das, Swapan Kumar.,and Sengupta, Sujit Ranjan., 2002a, Mineral potential map by a knowledge driven GIS modeling : an example from Singhbhum copper belt, Jharkhand, Proceedings of 5th annual international conference Map India 2002, New Delhi, pp 405-411.
- Mukhopadhyay, Basab., Hazra, Niladri and Mukhopadhyay, M. K., 2002b, Integration of exploration dataset in GIS using fuzzy inference modeling: example from Singhbhum Copper Belt, Jharkhand, India, GIS Development, July Issue, Vol 6, Issue 7, pp 18-21.
- Knox-Robinson, C. M. and Wyborn, L.A. I., 1997, Towards a holistic exploration strategy: using Geographic Information system as a tool to enhance exploration. Australian Journal of Earthsciences, Vol 44, pp 453-463.
- Knox-Robinson, C. M., 2000, Vectoral fuzzy logic: a novel technique for enhanced mineral prospectivity mapping, with reference to orogenic gold mineralisation potential of the Kalgoorlie terrane, Western Australia. Australian Journal of Earthsciences, Vol 47, pp 929-941.
- Sarkar, S.C., 1966a, Structures and their control of ore mineralisation in the Moinajharia-Mosabani-Surda section of the Singhbhum Copper Belt, Bihar. Contribution to the Geology of Singhbhum, edited by S. Deb, Jadavpur University, Calcutta, pp 75-83.
- Sarkar, S.C., 1966b, Ore deposits along Singhbhum Shear Zone and their genesis. Contribution to the Geology of Singhbhum, edited by S. Deb, Jadavpur University, Calcutta, pp 91-101.
- Sarkar, S.C., 1984, Geology and ore mineralisation of the Singhbhum Copper – Uranium Belt, Eastern India, Jadavpur University, Calcutta, p. 263.
- Sengupta, P. R.,1972, Studies of mineralization in the South-Eastern part of the Singhbhum Copper Belt, Bihar, Memoirs Geological Survey of India, Vol 101, p 82.
- Talapatra, A. K., 1968, Sulphide mineralization associated with migmatisation in the South Eastern part of the Singhbhum Shear Zone, Bihar, India, Economic Geology, Vol 63, pp 156-165.
Table 1: Fuzzy prospectivity value assigned to geological features of Singhbhum Copper Belt (After Mukhopadhyay et. al. 2002b).
| Lithology |
Favourable Contact |
Shear Zone |
Lineament and Fault |
Wall Rock Alteration |
Ground Geophysical(IP+SP+EM+Magnetic) |
| Lithological Unit |
Fuzzy pros. Value |
Contact between |
Fuzzy pros. Value |
Distance class in meters |
Fuzzy pros. Value |
Relationship with shear zone |
Fuzzy pros. Value |
Criteria |
Fuzzy pros. Value |
Characte-ristics |
Fuzzy pros. Value |
| (I) Chlorite schist/ quartz chlorite schist/ sericite quartz chlorite schist/ chlorite quartz schist/ Talc chlorite schist / soda
granite |
0.9 |
(i) Units of Group (I) of column 1 |
0.9 |
<= 500 |
0.9 |
(i) Within 250m and parallel to sub-parallel to shear zone |
0.5 |
(i) Two or more alteration type present |
0.5 |
(i) Strong ground anomaly |
0.9 |
| (II) Hornblende schist and Epidiorite |
0.8 |
(ii) Units of Group (II) and between Group (I) & (II) |
0.8 |
>500 - <=1000 |
0.8 |
(ii) Within 250m and not parallel to shear zone |
0.3 |
(ii) Only one alteration type present |
0.3 |
(ii) Weak ground anomaly |
0.2 |
| (III) Ultrabasics and mica schist |
0.6 |
(iii) Units of Group (III) and between Group (II) & (III) |
0.6 |
>1000 - <=1500 |
0.7 |
(iii) Outside 250m of shear zone |
0.1 |
(iii) No alteration present |
0.01 |
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| (IV) Other rock types |
0.2 |
(iv) Other rock types |
0.2 |
>1500 – <= 2000 |
0.6 |
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>2000 – <= 2500 |
0.5 |
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>2500 – 3000 |
0.4 |
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