Mineral potential map by a knowledge driven GIS modelling: An example from Singhbhum copper belt, Jharkhand
Geophysical Factor
Analysis of aerogeophysical data
Aerogeophysical surveys carried out by GSI under project “ Operation Hard rock” recorded electromagnetic, magnetic (TF) and radiometric ( total count) values. Aeromagnetic contours were traced out at 250 gamma interval, over relevant portion of the map, to project source of distraction on different lithologies.
As raw data was not available, the interpreted aerogeophysical anomaly maps was digitised and polygons containing the anomalies were selected to create the aerogeophysical layer of evidence. The aerogeophysical data represents a range from 700 to 4500 gamma values with a mean of 3108 gamma. An attribute score was added to the PAT table. The score is assigned to each polygon on the basis of the value in that particular polygon divided by 4500. This process generates a scale 0 to 1. Individual anomaly polygons were codified, weighted and rasterised on the basis of the score.
Analysis of radiometric data
Point radiometric (total count) anomalies were buffered using a buffer distance of 500m to generate the next layer of evidence. The radiometric data represents a range from 2400 to 100 gamma values with a mean of 500 . An attribute score was added to the PAT table. The score is assigned to each polygon on the basis of the value in that particular polygon divided by 2400. This process generates a scale 0 to 1. Individual anomaly polygons were codified, weighted and rasterized on the basis of the score.
Analysis of ground geophysical data
The ground geophysical survey was carried out in those areas which exhibited high gravity and aero-magnetic anomalies. The delineated high anomaly axes of Induced potential (IP), self potential (SP) and electro magnetic (EM) were buffered with a distance of 250m to generate the layer of evidence. An attribute score was added to the PAT file (Table3). Individual anomalies are rasterised on the basis of the score.
These three layers of evidence (aeromagnetic, radiometry and ground geophysical) were spatially combined to generate intermediate maps after assigning map score 5, 5 and 7 respectively. The intermediate map was divided by the total score (17) to produce the Geophysical Factor Map (Fig 2).
Table 3
| Ground - id |
score |
Characteristics |
| 1 |
9 |
Strong ground geophysical anomaly |
| 2 |
2 |
Low ground geophysical anomaly |
Geochemical Factor
Geochemical evaluation of an area leads to identification of potential host rock and possible source of mineralisation in that area. Secondary dispersion of elements in soil depends on degree of maturity, nature, thickness and the topographic expression of the area. Since topographic expression is not uniform in the area, a definite relationship of the soil and bed rock chemistry could not be established. Therefore, geochemical data of the bed rock alone was used for contouring to predict the primary dispersion of the copper. Geochemical anomaly maps of copper were digitised and copper values assigned for each polygon. The copper values range from 100 to 4000 ppm. An attribute score was added to the pat file and the value of the score of each polygon is assigned by dividing the copper value by 4000. Individual anomalies are rasterised on the basis of the score to create Geochemical factor map (Fig 2).
Structural Factor
Analysis of the shear zone
The line representing the maximum shearing effect was digitised. The shear line was differentially buffered at intervals of 500m and a new attributes score added to the PAT (Table 4). The shear zone formulated by this process, were rasterised to generate image on the basis of the score.
Analysis of lineaments and fault
Lineaments interpreted from LANDSAT imagery and regional structures (faults) were buffered using a buffer distance of 100m on each side. The polygons were reclassified according to their orientation and accordingly the score was added to the pat file(Table 5).
These two layers of evidence (shear zone and lineaments/faults) were spatially combined to generate intermediate maps after assigning map score 8 and 4 respectively. The intermediate map was divided by the total score (12) to produce the Structural Factor Map (Fig 2).
Table 4
| Shear-id |
score |
Distance from shear zone |
| 1 |
10 |
500m |
| 2 |
9 |
1000m |
| 3 |
8 |
1500m |
| 4 |
7 |
2000m |
| 5 |
6 |
2500m |
| 6 |
5 |
3000m |