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Integrating exploration dataset in GIS using fuzzy inference modeling
The procedure adopted on different GIS layers is summarised below.
Analysis of host lithology: The sulphide mineralisation is considered to be associated mainly with volcanics and metatuffs of Singhbhum and Dhanjori Groups. The geological map, originally containing 28 lithounits, was reclassified into four groups as per exploration model and assigned a fuzzy membership value of 0.9,0.8,0.6 & 0.2 respectively (Table 1).
Analysis of favourable contact: Detailed exploration reveals that contact between metasediments and metavolcanics are generally mineralised to a varied extent. The favourable contacts are buffered at an interval of 250m. The buffered polygons are reclassified into four broad classes according to its significance towards copper mineralisation and assign a fuzzy membership value of 0.9,0.8,0.6 and 0.2 respectively (Table 1).
Analysis of aeromagnetic data: The aeromagnetic polygon anomaly data represents a range from 700 to 4500 gamma. The fuzzy membership value of each polygon is obtained by dividing the gamma value of that polygon by 4500 (the highest value), thus, represents a range between 0 and 1.
Analysis of shear zone: The shear zone is represented as a line on the map. This line was differentially buffered at an interval of 500m and classified into six groups. The groups are assigned a fuzzy membership value from 0.9 to 0.4 depending upon its distance from shear zone (Table 1).
Analysis of lineaments and faults: Lineaments interpreted from Landsat imagery and faults marked on regional mapping were buffered using a distance of 100m. The polygons were reclassified into three categories according to its orientation and relation with the shear zone. The fuzzy membership values are assigned to those groups as 0.5,0.3 and 0.1 respectively (Table 1).
Analysis of ground geophysical anomaly: The groung geophysical anomaly axis of IP, SP, EM and magnetic were buffered with a distance of 250m. The buffered polygons are subdivided into two classes and assigned a fuzzy membership value of 0.9 and 0.2 respectively (Table 1).
Analysis of wall rock alteration: Four types of wall rock alteration described earlier are prevalent in the area. In a particular polygon which represents simultaneous occurrence of two or more alteration type, is assigned a fuzzy membership value of 0.5, whereas for the presence of one alteration type fetches value as 0.3 and rest is assigned a value of 0.01 (Table 1).
Analysis of bed rock geochemical data: Geochemical evaluation of an area leads to identification of host rock and possible source of mineralisation. In this process both primary and secondary dispersion pattern of element are evaluated and a relationship between them are established. A definitative relationship between soil and bed rock geochemical pattern is apparently not established in this area. Therefore, geochemical data of bed rock alone was used to generate primary anomaly polygons. The copper value ranges from 100 to 4000ppm. The fuzzy membership value of each polygon is obtained by dividing the copper value of that polygon by 4000.
Map combination using fuzzy operators
Map combination is an intuitive method where different primary and derived evidences are combined by a set of principles. For example, in this case, the evidence maps generated in earlier section can be combined in raster mode by a single or combination of fuzzy operators. A detailed inference diagram (fig. 2) is attempted to show how the different layers are combined and finally integrated by fuzzy operators. Here, the geological, geophysical and structural evidences are combined by fuzzy or operator to extract the maximum evidence from each layer. It also suggests that high value of any layer can be a useful evidence for copper mineralisation. Fuzzy algebric product operator is used in wall rock alteration layer to extract evidences for one or more alteration evidences. Finally all the evidence maps are combined by fuzzy gamma operator with gamma value as 0.95 to generate the final predictive map. Choosing of gamma value is subjective. As opined by Bonham-Carter (1994): to generate increasing effect of fuzzy membership values in the final map, gamma value need to be higher than 0.8. In this particular case, increasing gamma value higher than 0.95 bears very little effect on the final map (i.e. final GIS model). The final map grades the region into five subclasses in terms of suitability of finding copper occurrences i.e. highly suitable, fairly suitable, moderately suitable, less suitable and nonsuitable (Fig. 3).
Table 1: Fuzzy memebership value assigned to geological features of Singhbhum Copper Belt.
| Lithology |
Favourable Contact |
Shear Zone |
Lineament and Fault |
Wall Rock Alteration |
Ground Geophysical
(IP+SP+EM+Magnetic) |
| Lithological Unit |
Fuzzy Mem. Value |
Contact between |
Fuzzy Mem. Value |
Distance class in meters |
Fuzzy Mem. Value |
Relationship with shear zone |
Fuzzy Mem. Value |
Criteria |
Fuzzy Mem. Value |
Characte-ristics |
Fuzzy Mem. 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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Inference and conclusion
The final potential map or GIS model generated by deploying fuzzy inference engine are cross-validated by two different methods. Firstly, the known major copper occurrences / deposits are plotted on the final model i.e. potential map (Fig. 3). It is found that 60% of the deposits are lying on the high suitability zone. Secondly, it is also found that high suitability zone coincide well with the high bed-rock geochemical anomaly. Thus, it can be stated that the criteria and combination method chosen for this modeling fitted well with the existing ground reality. From the final predictive map it is inferred that the two clusters (marked as P) located west of Turamdih area are considered highly as the potential sites for further detailed exploration. It is worthwhile to mention that the areas west of Turamdih as potential areas for further exploration were also delineated by Mukhopadhyay et. al., 2002 by index overlay method. It is also to be noted that surfacial expression of mineralisation in these areas is less prominent. Hence, deeper probing by drilling or geophysical survey may be a good alternative.
Acknowledgement
The authors express their gratitude to all the officers who worked on Project Singhbhum and Project geoinformatics – Singhbhum Precambrian on 73J. They also remain grateful to DG, GSI for giving permission to publish this paper.
References
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- Anon, GSI, ER, 1991. Unpublished GSI report on Project Singhbhum – Synthesis of data of Singhbhum Copper Belt, Singhbhum District, Bihar: Part I & II., unpublished
- Bonham-Carter, G. F.,1994, Geographic Information System for Geoscientists: Modelling with GIS: Pergamon.
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- Mukhopadhyay, D. and Deb, G.K., 1995, Structural and textural development in Singhbhum Shear Zone, Eastern India, Proc.Indian.Acad.Sci, V104, no3: 385-408.
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