Estimation and evaluation of iron reserves in the eastern area of Eileh1 mine, Razavi Khorasan province

زمین شناسی اقتصادی

Volume 16, Issue 4 - Serial Number 43, 2024, Pages 101-124   

Document Type: Research Article

DOI: 10.22067/econg.2024.1103

Authors

Hamid Esmati Daroneh¹; Maryam Gholamzadeh^{* 2}

¹Extraction Unit Expert of Atta Metal Group of Industrial and Mining Companies, Mashhad, Iran

²Exploration Unit Expert of Atta Metal Group of Industrial and Mining Companies, Mashhad, Iran

Abstract

In the present study, we aimed to gain a comprehensive understanding of the Eileh 1 iron ore deposit located in Taybad City, Razavi Khorasan Province. A three-dimensional (3D) model was developed using specialized software, which incorporated lithological logs from boreholes, cross-sectional lithological profiles, and assay estimations covering the entire ore deposit area. The dominant lithology of the region consists of altered limestone and dolomite, as well as altered sediments, including sandstone, siltstone, and shale. To estimate the reserves, we employed both the classical inverse distance weighting (IDW) method and the geostatistical kriging method, tailored to the specific conditions of the ore deposit. Variogram analysis indicated that data values varied with distance and direction, revealing geometric anisotropy within the deposit. The maximum search radius in the direction of 0° azimuth was 42.6 meters, the average search radius at 90° azimuth was 10.6 meters, and the minimum search radius in the vertical direction (0° azimuth) was 32 meters. The dataset included topographic information and data from 177 boreholes, totaling 6,936 meters deep. Following geometric modeling and constructing a block model for the deposit, we estimated reserves for various iron-grade thresholds and compared the results of both methods. The findings indicate that the reserve estimates from the two methods were quite similar. While the inverse distance weighting method is recognized as one of the most accurate classical techniques for reserve estimation, the kriging method demonstrated greater precision and reliability. This is attributed to Kriging’s ability to account for the spatial structure of the deposit, its unbiased nature, and its lower estimation variance. The findings indicate that the reserve estimates from the two methods were quite similar. While the inverse distance weighting method is recognized as one of the most accurate classical techniques for reserve estimation, the kriging method demonstrated greater precision and reliability. This is attributed to Kriging’s ability to account for the spatial structure of the deposit, its unbiased nature, and its lower estimation variance.
 
Introduction
The evaluation of mineral reserves is conducted using various methods, which differ in calculation algorithms, accuracy, speed, the state of the mineral, and the characteristics of exploration activities (Madani, 1997; Ahmadi, 2010). All estimation methods require exploratory data analysis, and numerous statistical techniques can be employed to analyze this data. However, due to the detrimental effects of uncertainty on investment risk, it is essential to utilize the most effective estimation method grounded in precise data analysis techniques to minimize estimation error. Mineral reserve estimation methods can generally be categorized into two groups: classical (geometric) methods and geostatistical (statistical weighting) methods (Madani, 1997; Hassani Pak and Sharafodin, 2001; Ahmadi, 2010). Classical reserve estimation methods rely on traditional statistics and geometric calculations, while geostatistical methods, such as those based on regional variables, are grounded in spatial structure analysis and utilize the Kriging method. Among classical estimation techniques, the inverse distance weighting (IDW) method is recognized for its accuracy. For this study, both the ordinary Kriging method and the classical estimation method, specifically inverse distance weighting, were employed using two reserve estimation software programs: Surpac and Rockwork. The choice of methods is influenced by the state of the mineral, the extent and nature of exploratory work, and the available exploratory information, with estimation parameters selected through appropriate filtering techniques. In these methods, the deposit is divided into blocks with a square base, and mineral characteristics in each block are calculated based on the data distance within and surrounding it. Blocks must be designed to ensure that data is present within them. The statistical weight of each data point is considered in proportion to the inverse of its distance from the center of the block; thus, data closer to the center carry more statistical weight than those further away. The power of the distance typically varies from 1 to 3, with a common consideration of 2, which is why the method is termed inverse distance weighting.
The studied area is part of the Eileh 1 iron ore mine complex, located 49 km southwest of Taibad city in Razavi Khorasan province, covering an area of 240 hectares. The area is defined by longitudes ranging from 60° 22’ 40" to 60° 31’ 24" and latitudes from 34° 36’ 40" to 34° 41’ 45" (Figure 1). In the study area, a total of 177 exploratory boreholes were conducted using various methods, including reverse circulation (RC) (35 boreholes) and coring (12 boreholes), with an overall length of 6,936 meters.
 
Material and methods
The final step in the reserve evaluation process involves estimating the grade throughout the entire estimation space in three dimensions and calculating the reserve amount. The principles underlying reserve calculation methods are consistent; however, the primary differences among various methods lie in how the area is divided into segments and how their thickness and average grade are calculated. A comprehensive understanding of a deposit necessitates a series of processing and modeling operations (Hassani Pak, 2000). Due to the complexities involved and the lengthy, time-consuming calculations, this work is typically performed using specialized software, which offers greater accuracy, speed, and ease of use. Grade estimation methods can be categorized into two types: geometric and distance-based. Each of these methods has its advantages and disadvantages, and the selection of a suitable estimation method should be based on the underlying assumptions. In geometric methods, assumptions regarding the spatial distribution of grade within the deposit are taken into account; for instance, the variability of grade or thickness may be assumed to be linear. The software utilized in this research includes Rockworks and Surpac, both of which are comprehensive and powerful tools for imaging, modeling, and analyzing geological information and exploratory data, as well as for performing various modeling tasks based on the type and amount of data available. Additionally, the preparation of different sections from the mineral material was conducted by geologists and engineers. By employing these software programs, and through the drawing of boreholes, creation of cross-sectional profiles of lithology and grade, and development of a three-dimensional model of lithology, a thorough understanding of both the surface and depth of the deposit has been achieved. Furthermore, to calculate the mineral material reserve, the average density of the mineral material was considered to be 3.8 g/cm³.
The data investigated for modeling and reserve estimation are purely hypothetical and cover an area of 2.4 square kilometers. The existing drilling network in the region is based on local outcrops, with varying distances and trends. These distances have been minimized when addressing mineral matter and conducting more detailed investigations. A total of 177 boreholes have been drilled, of which 164 have encountered minerals, resulting in a cumulative borehole length of 6,936 meters. Among all drilled boreholes, the maximum depth is 96 meters, attributed to reverse circulation (RC) boreholes No. E1-RC-BH-9.2 and E1-RC-BH-27, while the minimum depth is 9 meters, associated with Rasol borehole No. E1-R-BH-57. The sampling intervals for iron are set at 50 meters.
 
Discussion and conclusion
The process of reserve estimation and the preparation of a block model for a mineral deposit involves distinct steps that must be executed deliberately and precisely. This approach ensures an accurate assessment and a suitable model, utilizing default values for reserve calculations within the software. However, certain applications may yield suboptimal results. Consequently, this research considers the mineral’s shape as a series of correct hollow shells, determining the maximum exploration radius, the minimum and maximum number of points used in block estimation, and the method of point exploration through range division.
In this study, three-dimensional quantitative modeling of mineralization (grading) and mineral reserves of the Eileh 1 iron deposit was conducted, utilizing data from well logs of exploratory boreholes and the grading of their drilling cores to evaluate the results. The estimation accuracy was assessed by comparing the results of reserve estimation using two classical methods: inverse distance weighting (IDW) with Rockwork software and kriging geostatistics with Surpac software. Figure 17 illustrates the grade-tonnage curve of the Eileh 1 iron deposit derived from these two methods. As anticipated, the figure demonstrates that as the limit grade increases, the deposit amount decreases while the average grade rises.
The findings of this research in the field of grading data modeling and reserve estimation for the Eileh 1 iron deposit, utilizing distance squared and kriging methods in Surpac and Rock Works software for a grade limit of 17% (as shown in Table 2), indicate that the calculated reserve amounts derived from different methods and software show no significant differences overall. As illustrated in Figure 17, the graph of the average grade calculated by both methods and across the two software platforms aligns closely, with variations in tonnage charts primarily reflected in the slope of the graph line at specific grades. Although the results from these two methods are largely consistent, the inverse distance weighting method is recognized as one of the most accurate classical reserve estimation techniques. While this method offers numerous advantages, such as high accuracy due to the blocking of the deposit, it does have a drawback: in the case of low-grade deposits, the mixing of ore and tailings within the blocks tends to be high, leading to reserves being estimated slightly above their actual value. Conversely, the geostatistical method of kriging, which considers the spatial structure of the region, is more precise and boasts a higher degree of reliability due to its unbiased nature and minimal estimation variance. Vairographic studies, conducted by plotting longitudinal variograms in various directions, revealed that the deposit exhibits geometric anisotropy, with the highest elongation observed along the X-axis and the lowest along the Y-axis (Figures 9c and 10).
Based on the results obtained from the modeling of the Eileh 1 deposit and the available information regarding areas that were inaccessible during the initial excavation but will be accessible in the future, it is recommended to drill new boreholes in a regular grid pattern. This approach will facilitate a more accurate determination of the mine reserves. The design and implementation of this drilling strategy should be carefully planned to optimize resource assessment.
 

Keywords

Reserve estimation; Kriging; Inverse distance weighting (IDW); Eileh 1 iron deposit

References

Aghanabati, A., 2006. Geology of Iran. Geological Survey of Iran, 586 pp.  Retrieved January 2004, from https://www.researchgate.net/publication/292808705_Geology_of_Iran_Tehran_Iran

Ahmadi, R., 2010. Application of statistical patterns for ore reserve estimation emphasis to Ali-abad, Yazd copper mine. Arak University of Technology, Arak, Report 1, 102 pp. (in Persian with English abstract)

Ahmadi, R. and Baharlooei, Z., 2021. Qualitative and quantitative evaluation of geophysical data accordance with assay data in Yazd Darreh-Zereshk copper deposit to propose optimal location for new exploration boreholes. Journal of Engineering Geology, 15(1): 1–34. http://dx.doi.org/10.52547/jeg.15.1.1

Daya, A.A., 2012. Reserve estimation of central part of Choghart north anomaly iron ore deposit through ordinary kriging method. International Journal of Mining Science and Technology, 22(4): pp. 573–577.  https://doi.org/10.1016/j.ijmst.2012.01.022

Ghajar, Y. and Soltani-Mohammadi, S., 2015. Statistical and geostatistical modeling of Fe, S and P grade in Jalal-abad Iron ore mine. 33rd National Geosciences Symposium, Geological Survey of Iran, Tehran, Iran. Retrieved January 2015, from https://www.researchgate.net/publication/274564712_mqaysh_rwshhay

Hassani pak, A.A., 1998. Geostatistics. Tehran University Press, Tehran, 314 pp. (in Persian)

Hassanipak, A.A., 2000. Modeling of metallic and nonmetallic deposits and their exploration application. Tehran University Press, Tehran, 512 pp. (in Persian)

Hassani Pak, A.A., Sharafodin, M.S., 2001. Exploration data analysis. Tehran University Press, Tehran, 987 pp. (in Persian)

Madani, H., 1997. Principles of prospecting, exploration and evaluation of ore reserves. Khane Farhang, Tehran, 816 pp. (in Persian)

Rasera, L., Felipe, L. and Costa, C., 2015, A conflict-free, path-level parallelization approach for sequential simulation algorithms; Computers & Geosciences, 80: 49–61. https://doi.org/10.1016/j.cageo.2015.03.016

Rooki, R., Mohammadi, M.N. and Safari, M., 2022. Reserve estimation of IV deposit of Sangan Iron ore mine using geostatistical method and SURPAC software. Journal of Mining Engineering, 17(56): 21–39.  https://doi.org/10.22034/ijme.2022.537596.1879

Salsabilian, F., 2006. Iron deposit reserve estimation of northern anomaly of Bafg using indicator kriging. 5th Students Mining Engineering Conference, Isfahan University of Technology, Isfahan, Iran.  Retrieved April 2006, from https://civilica.com/doc/9735

Sharif, A.J. and karim-zadeh, V., 2015. Variography of exploration data of Korkora2 iron ore deposit for determination of required parameters in order to reserve estimation. 33rd National Geosciences Symposium, Geological Survey of Iran, Tehran, Iran.

Sharifi, H., 2023. Geological map report 1:1000 hematite Eile 1, Negin Kani Ehya Industrial and Mining Company.

Surur, A.N., 2008. Surveying, modelling and visualisation of geological structures in the Tunberget tunnel. M.Sc. Thesis, KTH Royal Institute of Technology, Stockholm, Sweden, 75 pp.

Ternet, Y., Guillou, Y. and Maurizot, P., 1978. Geological map of Iran, 1:100000 Series, Sheet 8059, Khaf.

Yousefi, M. and Khaloukakai, R., 2015. Computer use in mining (exploration, extraction, geology and Mapping. Jihad Publications, Amirkabir Industrial University, Tehran 287 pp.

Zakary, M., Rasouli, V. and Tokhmchi, B., 2005. Geostatistical modeling and reserve estimation of Gol-Gohar iron ore (3) anomaly using Surpac software. 9th Symposium of Geological Society of Iran, Kharazmi University, Tehran, Iran.