A.K . Ray & B. Mukherji
Geological Survey of India
Calcutta – 700064
Email: [email protected]
The Singhbhum Copper Belt (CBM), a curvilinear zone of approximately 160 km length between Kharswan-Duarpuram in the west and Baharagora-Kesharpur in the east, in Bihar state of India, is a well known repository of copper, uranium and apatite-magnetite mineralisation, related to extensive shear effect caused by structural phenomena involving rocks of the lower Proterozoic age (C. 2400-2300 Ma). Drawing upon the geological and the aero-geophysical data on the mineralised belt from extensive information base generated over years of study by the Geological Survey of India and other exploration agencies and the academia, the parameters for probable mineralisation were identified from the known mineral occurrences in the Belt.
A dual approach was then made to prepare a probability model for targeting mineralisation in the Central Sector of the Belt better known for the known deposits. The first approach was to use a total of 122 geological variables in a stretch of the area between Baharagora and Tamadungri.
Two types of statistical analysis was done, to select or identify variables which were important for the analysis and to obtain an equation connecting the metal accumulation with the other variables. A network of cells each of 1km x 1km area was superimposed on the geological map and the variables present in each cell were noted. The reserve figure of copper and their grade in 15 locations were collected and total metal accumulation in each was calculated. The results were scaled, summed for larger unit cells and contoured to give a probability index.
From the database, statistical techniques employed to reduce the number of variables were done automatically with the help of programs written in Dbase V and SPSS and interfacing them. The grids and the probability contour map were also generated with the help of computer.
The result indicate possibility of new mineral deposits in the unexplored areas lying between known deposits in the Singhbhum Copper Belt. Six new areas were identified by this approach.
The second approach was to generate a spatial model by subjecting the (a) geological map, (b) the aeromagnetic total intensity anomaly map and (c) the Bouguer gravity anomaly map over the shear zone to digitization and integration in the ARC-INFO GIS environment. Using the range of values for each parameter in the known deposit areas, the integrated combination focused on areas with potential for mineral find, requiring validation by ground exploration methods.
The Singhbhum Copper Belt in Bihar is well known for its wealth of mineral resources, mainly of copper and also of uranium, apatite-magnetite and kyanite. The copper producing mines of this belt under Hindustan Copper Limited (HCL) leasehold form the major contributors in the production of copper from Eastern India. This belt with indications of copper mineralisation stretches for an approximate strike length of 160 km from Duarpuram-Kharswan in the west to Baharagora-Kesharpur in the southeast (Fig.1). The economic occurrences are known only in parts of the central sector, which constitutes about 15% area of the total stretch. To locate new targets, volumes of data on geological, geophysical, geochemical and mineral investigations were generated over the last few decades. These were compiled, collated and synthesized by GSI under ‘Project – Singhbhum’, 1991 with special emphasis on identifying the gaps in information and knowledge, and locate the areas which deserve follow up action.
The present work attempts at analyzing the data of the known variables in copper producing central sector drawn from the data bank, created by the ‘Project – Singhbhum’, 1991, by digitizing the mapped data and subjecting them to (a) statistical analysis and (b) GIS, to create probability models statistically and spatially using for the first, the geological parameters only and for the second, the geological and geophysical parameters in an interactive manner.
Background Geological and Geophysical Information:
Before proceeding to the analytical part of the work, it is necessary to give a geological and geophysical background of the terrain under scrutiny.
Geologically, the area forms a part of the Proterozoic Singhbhum Mobile Belt (C.2300-2400 Ma) bordering the northeastern part of the Archaean Singhbhum – North Orissa Iron Ore craton. The cratonic block consists of granite-greenstone assemblage comprising both intrusive and volcanic rocks of different phases. The Lower Proterozoic Dhanjori basin comprising volcano-sedimentary rocks occur between the granites in the south and Lower Proterozoic metasediments and metabasics of Singhbhum Group in the north. The Singhbhum Shear zone runs close to the interface of the Dhanjori Group and the Singhbhum Group of rocks and is associated with a host of minerals. All the group of rocks of this belt uniformly dip towards north at moderate angles. Three phases of deformation have affected the rocks of the shear zone. High-grade granulite facies has been attained in the rocks lying north of the shear zone while the rocks south of it have yielded low-grade greenschist facies metamorphism.
The area of study for the present statistical analysis is kept restricted between Baharagora in the south east Tamadungri in the west along the shear zone having an average width of 18km (Fig.2) in view of consistence in the raw data availability and the occurrence of known mineral deposits with mining activity.
Prospecting in different parts of the Copper belt has revealed that copper sulphides occur in almost all types of rocks in the shear zone. These can be broadly grouped under the following categories: (1) Metasediments e.g. quartzite, mica schist etc., and their derivatives; (2) Metabasic rocks; (3) Soda granite or other granitoid rocks and (4) Meta-ultrabasic rocks and their derivatives.
The mode of occurrence of the sulphides as noted by different workers are: Massive veins, braided veins, stringers, composite veins, dissemination, discordant irregular bodies, sheet like bodies and branching and interconnected en-echelon lenses and layers.
Various authors suggested different structural elements for control of mineralisation. The shear zone/thrust zone is the structural control for localization of ores (Dunn, 1937), a set of cross fold is the controlling structural element (Narayanaswami, 1959). It was suggested, Sen Gapta et.al.(1961); Sen Gupta, (1965,1972) that ultimate control for localisation of sulphide minerals, in all scales, was by two planner structures described as gently dipping ‘slip plane’ (first designated as ‘S1’ and later as ‘S5’) and steeply dipping ‘cleavage’ or ‘schistosity’ (first designated as ‘S’ and later as ‘S2’).
Information on surface indication, host rock, structural set up, mode of occurrence, control of mineralization, mineral assemblage, paragenesis, geochemistry, lithology etc. for certain locations were collected from data compiled by Anon, GSI, ER (1991) on data sheets shown in Table-2. These data sheets formed the basis for building up the computerized database.
The reserve of copper and their grade in different mines and prospects as compiled from the reports submitted by Robertson Research, Australia to HCL are given in Table-1. This information was utilized for calculating the total metal accumulation in these locations.
Both airborne and ground geophysical surveys have been carried out extensively in this belt. The geophysical data of the area have been collated from the airborne geophysical survey report of Project ‘Operation Hard Rock’, GSI, AMSE (1968), published gravity map of NGRI (1981), gravity – magnetic studies of Pathak, et.al., GSI (1989-90), and report on ‘Project – Singhbhum’, GSI, ER (1991).
The airborne geophysical survey was done employing magnetic (TF), electromagnetic and radiometric (total count) methods. Ground geophysical surveys include magnetic (VF), gravity, electromagnetic, IP, SP and resistivity methods.
The airborne magnetic map reveals that a chain of low amplitude high intensity magnetic closures follow the trend of the copper mineralized zone. The area occupied by Singhbhum Granite is outlined by 3000 gamma contour. The mapped outcrop of soda granite/feldspathic schist is conformable to the trend of aeromagnetic closures. The radioactive zones have been found to be in excellent correlation with the known mineralized zones. Both radiometric and magnetic response are continuous along the strike extension of Turamdih, Dhadkidih and other prospects, which are related to uranium and copper mineralisation.
The area has picked up the usual negative gravity anomalies. Two prominent trends of Bouguer gravity contours are indicated (i) Over Singhbhum Group of rocks and Dalma volcanics having E&W trend and (ii) over Iron Ore Group of rocks and Dhanjori volcanics with N-S to NW-SE direction. The Dhanjoris are indicated as Bouguer high. The mineralized zones from Kanyaluka to Rakha Mines have yielded linear high Bouguer gradient. This linear ‘high’ zone abuts against a gravity low (-39 m. gal) at Jaduguda, which is a uranium producing sector. From Jaduguda the high Bouguer gradient continues upto Nandup at the western end of the study area. Almost all the Copper Mines and the mineralized zone of this belt are located within this feature of high gradient. The feature that this high gradient Bouguer gravity does not follow the shear zone in the west beyond Rakha mines upto Jaduguda may be explained by the change in mineralogical milieu from a predominantly sulphide minerals at Rakha to predominantly radiometric minerals at Jaduguda.
The ground magnetic (VF) data is compatible with the airborne magnetic (TF) pattern. The economic Cu mineralized zone from Badia to Rakha is reflected as an isolated low amplitude magnetic highs in an axis. This axis corroborates with high gravity gradient as discussed above. Another similar magnetic axis south of the earlier one has been located over Dhobani extending along NW upto Dhanjori Pahar through Tamajhori, Kasaidih and SW of Patkita. Recent exploration by GSI has proved economic deposits at Dhobani-Tamajhori sector as footwall lodes. From this area onward, this magnetic axis gradually turns southwest and finally almost merged with the N-S gravity feature over Singhbhum Group of rocks. Both gravity and magnetic components also show an excellent correlation in the far southeastward extension of this belt around Kesharpur (Chaudhuri, et.al., 1996).
Host Rock Lithology:
The copper sulphides associated with other minerals occur mainly in the Dhanjori metavolcanics and their derivatives, and the feldspathic schist/soda granite and metabasics, chlorite schist, sericite schist, mica schist and quartzites of Singhbhum Group (Chaudhuri, et.al., 1998).
Two statistical techniques were employed for the study. The first technique was a “Characteristic Analysis” developed by Botbol (1971) and the second technique was an application of the multiple regression analysis as described by Agterberg (1972). At various stages of the work, different standard softwares for computer processing of the data, were utilized and attempt was made to automate the whole process. The idea was to develop a software which could be routinely used by any geoscientist, for any area having known mineral deposits, to predict probability of occurrence of deposits in the area of interest. For the present study a database was developed using dBase V software.
An important aspect of the database is that any number of groups of variables can be added in the database simply by incorporating the variables with their code in the form of a table e.g., Geophysical and Geochemical variables can be incorporated (which are not at present considered) by simply adding Geophysical and Geochemcial tables with their coded variables.
Broadly the variables selected were grouped into seven classes (Annexure-I)
- Lithology consisting of 18 variables like soda granite, chlorite-quartz schist etc.
- Surface indication consisting of 4 variables like gossan, old workings etc.
- Structure consisting of 5 variables like foliation, lineation etc.
- Ore mineralogy consisting of 22 variables like chalcopyrite, pyrite etc.
- Host rock consisting of 41 variables like biotite schist, sheared conglomerate etc.
- Mode of occurrence consisting of 13 variables like stringers, specks etc.
- Control of mineralization consisting of 19 variables like lithological and structural, localized along axial plane of fold etc.
So a total of 122 variables were considered. It is assumed that all these variables are present throughout the area and for the time being, are of equal importance. After scanning the literature on Singhbhum Copper Belt, 27 locations were selected from where the variables were considered for statistical treatment. The following two statistical techniques have been adapted for the present work.
In order to find out those variables most representative of a particular mineral in a particular area or in other words those variables which were always or nearly always found to be associated with copper in the study area and to reduce the huge number of variables characteristic analysis was carried out. The total number of variables were brought down to 20 from the original 122.
The whole process, right from creation of the database to ranking the variables was automated with the help of two softwares, dBase V for Windows and SPSS statistical package. Programme was written in dBase V for Windows which manipulated the data from the database created earlier and transformed the data into binary form. The data matrix M1 thus created was called the presence-absence matrix. This matrix was then automatically transferred to SPSS, where the matrix was manipulated to generate the ranked variables. The 20 variables so produced are shown in Table-7.
The geology of the area selected for the study was scanned from the geological map of Singhbhum Copper Belt, Fig.2. It was found that out of the 20 geological variables some were not mapped separately. For example sericite-chlorite-quartz schist and quartz-chlorite-biotite schist were not mapped as separate units but as one unit and so could not be differentiated. It was mapped as quartz-biotite-chlorite-schist and this variable was considered. Too the final analysis the variables were brought down to only eight (8).
- Quartz chlorite schist.
- Quartz biotite-chlorite schist.
- Mica schist/phyllite.
- Foliation(S1), cleavage(S).
- Pucker lineation/Plunge of fold axis.
New variables were formed by combining the above 8 variables (Botbol, 1971; Vyshemirshy et al., 1971) into 28 such new variables by taking 2 variables at a time. So the total variables used for the analysis was 36. The reason for combining the geological variables is to allow for interaction between different variables which is so common in geology.
Calculation of Probability Index:
In a region where there are known mineral occurrences with gap areas in-between, the potential of the whole region including the gap areas for a particular mineral, say copper, can be statistically calculated. Instead of calculating one value for the whole region, the area was divided into a number of smaller areas at regular spacing and the potential or probability of, say copper, which occur in such smaller areas can be calculated by giving it a value called Probability Index. The Probability Index will be 100 over the producing mines and will vary from 0 to 100 over the gap areas. Where the Probability Index will be high in the gap areas, it will indicate possibility of finding mineral occurrence. These values were be contoured to give probability contours.
The geological map of the study area was considered for transforming the above 36 variables into binary form. With the help of AutoCad software a grid of 1 km x 1 km was made on a tracing paper and the tracing paper was overlain on the geological map. For each cell, over the area, the variables present in that cell were noted. The reserve of copper in the different deposits and their grade was found out from published material. Then the total metal accumulation was calculated for each of these areas with help of the formula Grade/100 x Reserve, 15 locations were considered where there is a producing mine and where metal accumulation could be calculated. The cells in which these locations fell were called the control cells. Normally it was assumed that one location fell in one cell. The adjacent cells where it was assumed the maximum exploration has taken place were called the control area. Out of the 36 variables those falling in each of the control cells were noted and if present was denoted by 1 and if absent was denoted by 0. So a matrix was prepaired, Table-8, with the 1st. column being the 15 locations, 2nd. column the metal accumulation in these locations and the 1st row being the 36 variables. A further data reduction was done by discarding those variables which were 0 in all the 15 locations i.e. the variables were absent in these locations. So the number of variables considered came down to 26.
Once this matrix was generated stepwise regression was carried out to obtain a relation between the metal accumulation and the variables.
Multiple Regression Analysis:
This is done to calculate the metal accumulation in the unknown cells where the variables are known. Muliple regression analysis relates the independent variables i.e. the variables that are being considered to the dependent variable that is the metal accumulation by giving an equation relating the two.
The equation was applied to each cell over the area under study and the metal accumulation value in each cell was calculated. This calculation was done using a spreadsheet software. Program was written to automate the calculation. The values in each cell were multiplied by a factor of 100 and rounded.
Within the study area the total number of control cells is 16, and the total number of cells in the control area is 90. In order to assign values to contours the following procedure was adopted :-
- The sum of all calculated values in the control area was taken;
- This sum was divided by the number of control cells i.e. 16. This gives the scaling factor of 68;
- The calculated values for all cells in the area was divided by the scaling factor i.e. 68.
- The values for overlapping blocks of 4 cells were added.
- The results were contoured using a contouring software, SURFER.
The contour so generated was imported to another software, AutoCad where it was overlain on the 1 km x 1 km grid after bringing both of them to the same size.
From a large number of variables considered, Characteristic analysis helped in reducing the variables and selecting the important ones. The Multiple Regression Analysis helped in further reducing the variables and ensuring that only those variables were used which were important for the analysis. This technique helped in giving an equation relating the metal accumulation to the variables and this aided in predicting the metal accumulation in unknown areas.
The average amount of copper per control cell is calculated to be 96,128.13 tons. The area of the unit cell is 2 km x 2 km or 4 sq km. Hence, the probable tonnage of copper per square km (K) amounts to (96,128.13 x m) / 4 = 24,032 m.
Hence, from the contoured map (Fig.3 and Fig.4) it can be said that where the probability index contour is 0.9, the probable tonnage of copper in the surrounding 2 km x 2 km cell is 0.9 x 96, 128.13=86,515.317 tons or say 86,500 tons. From the given table we know that the estimated value 0.9 is reasonably precise, i.e., at least 1,2 or 3 cells will have 86,500 tons of copper with a probability of 64%.
Evaluation from the Statistical Studies:
A preliminary study of the probability contours shows some remarkable results. The contour highs follow the general trend of the Singhbhum Copper Belt. The highs are located near producing mines. Some highs have come in areas where there are no known mineral deposits. Six new locations have been demarcated where there is possibility of copper mineralisation, some of them are near Ujalpur, Nimdih, Sidheswar etc. From the disposition of the highs it appears that a second parallel shear zone is present approximately parallel and south of the present shear zone. These are likely locales for search of new deposits. It is advisable to corroborate these high probability zones with geophysical and geochemical anomaly data as also with the geology of the total area.
Spatial model from Geological and Geophysical data using GIS:
The compiled geological map of the Singhbhum copper belt in its central sector in 1:50,000 scale and the corresponding Aeromagnetic total intensity map including the radiometric data and the Bouguer gravity anomaly map over the terrain, both in 1:250,000 scale resampled to same pixel size, were digitized using an A0 size digitizer. The geochemical data not being available uniformly over the area of study have not been digitized for GIS purpose. The digitized geological-mineral map was related with a database, which helped in processing of the map. These data together with the digitized data of topographic features, road layout, river courses and location of prominent places served as base map for the project (Fig.5). These digitized information constituted a sub-file (layer) and stored. Similarly the aeromag contours and radiometric data (total count of U, Th & K) were digitized and formed another sub-file and the Bouguer contours formed the third (Figs.6 & 7). All the digitized data were entered into the computer memory through ARC-INFO GIS for analysis.
Mineral potential area over parts of Singhbhum Shear zone
These data in digitized form were merged with one another in layer form using the common database for an-interactive integrated analysis. The merged data outputs of Geology and aeromagnetic & radiometric (Fig.8), geology and gravity (Fig.9) were generated to see the correlation between geology and aeromagnetic-radiometric on one hand and geology and gravity on the other.
An algorithm was developed for a spatial query to find out name and other details of mineral occurrences and characteristics of geophysical parameters in the selected neighborhood of interest. The GIS (PC ARC-INFO) provided tools for two types of interactive query. One was like “what are the characteristics of a location?” and the other was “where do these characteristics occur ?”.
For the first one, for example, some places on the geological map of Singhbhum was identified on the video monitor, one might wish to know the detail of the rock formation, distance from the thrust zone, value of the Bouguer anomaly, the aero-magnetic total field intensity value and so on. A summary table could be created and related to that location.
For the second type of query the question may be related to distance, orientation etc. Condition may be like “Find all occurrences of say, sandstone / Quartz-Conglomerate of Dhanjori group”. This helped creating thematic map of an area for each individual or combination of themes (Fig.10).
It was noted from this analysis that the copper bearing mineralized areas in the central part of the Singhbhum shear zone are marked by moderate to low aeromag anomaly and high Bouguer gravity values, as was interpreted from a visual analysis of data earlier (Chaudhuri et.al., 1995-1996).
From the merged data in digital form and with the spatial query software. the geophysical signatures within one km buffer of the thrust zone (Fig.11) was obtained by clipping the aeromag or the Bouguer map with 0.6 km buffer zones across the copper belt thrust (fig.12 & 13).
Instead of a standard statistical probability modeling using multiple parameters for mineral prognostication, this GIS analysis used the query method to create a spatial model by superimposing all the three buffer zones on to a single map to create intersection zones with known mineralized locations(Fig.14). Using the criteria of integrated data over the mineralized areas the entire map was then generated with the same parameters showing the probable target locations(Fig.15).
The project is not aimed at a complete mineral targeting study, but primarily aimed at a GIS application, using the indigenous facility of a digitizer, ARC-INFO GIS software and printer-plotter. The output data products show that some success has been achieved towards this goal. The methodology used in this study could be adopted for similar studies in other locations using multiple thematic information for environmental analysis, hazard mitigation studies etc, besides mineral targeting.
The two models for mineral localisation search applied in the study were aimed at (1) a computerised approach to determine statistical probability index based on the geological parameters in the known mineral locations within the zone of Singhbhum Shear and (2) a GIS approach by combining the geological and geophysical parameters over the same zone to identify the spatially indicative locales of mineralisation.
The two approaches are complementary, but at the same time corroborative of each other, in the sense that both the techniques have brought out a probable second line of shear south of the main one with indicative combination of mineralisation prameters.
The techniques presented here are, by no means, a part of the mineral exploration programme in the area of study.
It is only an experiment to test the utility of the statistical and GIS methods in a well known mineral belt using the available information base. The model study presented here may however, be utilised in some other less known shear zones in the Singhbhum-Chhotonagpur mineralised area.
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The authors are grateful to Dr. S.K. Mazumder for initiating the project in the Eastern Region when he was the Deputy Director General in the Eastern Region. Subsequently, the Project found strong patronage from Shri Devashis Chatterjee, Deputy Director General, Eastern Region, who took keen interest in the digitization and GIS application validation work, the main purpose of the project, for which the authors are grateful to him. The authors are also greatful to Shri B. Chaudhuri, Geologist(Sr) and Shri S.K. De, Geophysicist(Sr) for their respective roles in providing the background information and handling of GIS (ARC-INFO) respectively. Finally, the cooperation and involvement of S/Shri Tapas Roy and Utpal Bhattacharya, Draftsmen, who have painstakingly learnt and carried out the digitization and data integration through GIS, are highly appreciated.