Geological-exploration modeling of the North-Narbaghi copper deposit, Saveh and reserve estimation using blocking, 2D grid model and 2D accumulation approaches

Document Type : Research Article

Author

Mining Engineering Department, College of Geosciences Engineering, Arak University of Technology, Arak, Iran

Abstract

Introduction
Deposit modeling includes various types of descriptive-genetic, geometric, geostatistical modeling-simulation, economic, and mathematical analyses (Erickson, 1992). In the present study, 3-D geological, mineralization and ore deposit mathematical modeling of the North-Narbaghi copper deposit, Saveh were carried out using various capabilities of the Rockworks software package. The North-Narbaghi exploration area is located in the northeast of the Markazi province, ~26 km northeast of Saveh city, at 1:100,000 Zavieh sheet. The monzogranite-quartzmonzodiorite intrusive bodies are the main host rocks for mineralization in the area. Two main porphyry copper mineralization zones consisting of phyllic and potassic alterations have been recognized in the area by applying systematic explorations including 23 boreholes (i.e., NNB1 to NNB23) with the total depth of 2425 meters. Five boreholes have been drilled in the eastern stock whereas 18 boreholes are located in the western part. The drillholes range in depth from 52 (e.g., NNB9 borehole) to 224 meters (e.g., NNB1 borehole). A total of 558 drill cores collected from different boreholes were analyzed for their copper and associated elements. The ore grades typically range from 2ppm to 12.2%.
 
Materials and methods
The RockWorks software package calculates the volume of minerals in two ways; one through the borehole manager window, the I-data menu, the volumetrics submenu, and four other paths in the utilities window called Ez-volume, 2D (grid model), grade block model grade thickness (GT) grid, and compute grade-thickness volume & mass which comprise subsets of volumetrics main menu. In all cases, the average density of the mineral was considered to be 2.65 g/cm3. Also, the SGeMS software (Remy et al., 2009) outputs were used in order to obtain more accurate estimation of grade and tonnage of the deposit, if required, for estimation of parameters such as search radius. In this research, the values of the search radius corresponding to the measured, indicated and inferred reserve categories were assumed to be 50, 130 and 433 m, respectively. For the North-Narraghi copper deposit, six cutoff grades of 1000, 1500, 2000, 2500, 3000 and 3500ppm were defined. In the 2D (grid model) method, the volume of the mineral deposit was calculated by gridding the thickness of the deposit. The cell size of 20*20*2m was selected and the number of data involved to estimate each cell was chosen to be 3 based on the borehole distances and depth of drill cores by trial and error.
To evaluate ore deposits, sometimes accumulation parameter grade-thickness (GT) was used instead of cutoff grade. In this operation, for each column of cells within the primary grade model, the sum of the GT values of the cells were calculated and stored as GT values within the grid model. These values of cellular GT are calculated by multiplying grade by thickness (height) of the cell. If the grade value of a cell is less than the threshold value defined by the user, the value for this cell will not be taken into account in the total summation. If the value of the final sum is lower than the threshold defined by the user, the program will set the value of the relevant grid to zero. The minimum acceptable values for the studied ore grade for the six defined cutoff grades of 0.1%, 0.15%, 0.2%, 0.25%, 0.3% and 0.35% and also, accumulation threshold corresponding to the minimum acceptable values of grade multiplied by the minimum core length (0.1 m) was defined to be 0.01, 0.015, 0.02, 0.025, 0.03 and 0.035(m%), respectively.
                    
Results
The ore reserve calculated by 2D GT method shows that the software output is slightly different from that of other techniques. This method calculates the net ore reserve for three categories of "measured", "indicated" and "inferred" categories. In this algorithm, reserve calculations for the study area has not assigned any values for the "inferred" reserve category. Moreover, no reserve has been calculated for the "indicated" category by increasing the cutoff grade value. In other words, there is no reserve in the "inferred" category for the various cutoff grades. There is no reserve even in the "indicated" category for the upper limits cutoff grade. This indicates the sensitivity of the applied algorithm to the degree of reliability of the reserve.
Assay data modeling and ore reserve estimation using the variety of methods that exist in Rockworks for 6 cutoff grades of 1000, 1500, 2000, 2500, 3000 and 3500ppm show that in some cases the results of various methods are very different. In general, blocking through I-Data menu and 2-D accumulation (2D GT) methods are more accurate than the others available in Rockworks to estimate the ore reserve of the study area. Overall,
reserve value was calculated about 500000 tonnes with an average grade of 0.8% for cutoff grade of 0.1% (1000ppm) by averaging the ore reserve and average grade of the deposit and using conventional ore reserve estimation methods.
 
Discussion
The findings of the current investigation confirm that the feasibility of achieving reasonable, valid, and reliable results using a specialized software is highly dependent on the knowledge and experience of the user and the high degree of validity of results is only obtained by the choice of appropriate modeling methods as well as selecting suitable parameters.
The results of this research study especially how to select parameters in different parts of the software can be generalized for modeling other metallic deposits similar to the study area. However, validation of modeling operation and the produced models are highly dependent on the type and amount of available exploration information.
 
References
Erickson, Jr.A.J., 1992. Geological interpretation, modeling and representation. In: H. Hartman (Editor), SME Mining Engineering Handbook. SME-AIME, New York, pp. 333–343.
Remy, N., Boucher, A. and Wu, J., 2009. Applied Geostatistics with SGeMS: A User's Guide. Cambridge University Press, New York, 284 pp.

Keywords


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