Elsevier

Ore Geology Reviews

Volume 74, April 2016, Pages 15-25
Ore Geology Reviews

Regional mineral resources assessment based on rasterized geochemical data: A case study of porphyry copper deposits in Manzhouli, China

https://doi.org/10.1016/j.oregeorev.2015.11.009Get rights and content

Highlights

  • Regional geochemical survey data was rasterized and a geochemical atlas generated.

  • Image pixels were determined based on geochemical exploration sample point spacing.

  • Hydrothermal alteration, denudation, and mineralization intensity were determined.

  • Resource estimation was conducted through regression analysis.

  • Results show that the method provides higher prediction precision.

Abstract

This study used regional geochemical survey data (1:200,000 scale) from the Manzouli area of China to assess mineral resources. Geochemical survey data was rasterized and a geochemical atlas was generated, with the image pixel size determined according to geochemical exploration sample point spacing. The Wunugetushan, Babayi, and Badaguan porphyry copper deposits were selected as model areas for the assessment of copper mineral resources. Three parameters were considered for the calculation of the mineral resources. An ore-bearing hydrothermal alteration coefficient was determined based on geological characteristics and geochemical characteristics of the model area, in order to determine alteration intensity; a denudation coefficient was calculated to determine denudation extent; and a mineralization intensity coefficient was calculated to determine the intensity of mineralization within each pixel. Resource estimation was conducted through regression analysis of model deposit resources and coefficients. The results can be used to determine prospecting target areas based on frequency classification and can be used to estimate the number of ore deposits. Results show that resource estimation using rasterized geochemical data provides high prediction precision and accurate positioning.

Introduction

Mineral prospectivity analysis is used for regional exploration targeting. While mineral prospectivity analysis is intended to identify where undiscovered deposits are most likely to be located, quantitative resource estimation is key for estimation of how much metal is likely to be contained in these undiscovered deposits. Porwal and Kreuzer (2010) provided an overview of the history, status, and future of mineral prospectivity analysis and quantitative resource estimation.

Mineral resources assessment, the qualitative and quantitative assessment of mineral resources based on different scales of geochemical survey data, includes geochemical quantitative estimation. As early as the last century, American scholars noted a linear relationship between the abundance of 26 crustal elements and recoverable reserves of tonnage in the US (McKelvey, 1960); other scholars then pursued related research, with many notable achievements (Garrett, 1978, Mookherjee and Panigrahi, 1994, Nishiyama and Adachi, 1995).

Solovov (1957) studied the calculation of metal resources in dispersion haloes, the estimation of displacement, and the evaluation of ore-bearing properties. He also founded a method for estimating surface metal resources. European and American scholars established abundance relationship models applicable to rock type units or to regional or geological provinces (Celenk et al., 1978); Zhao (1991) proposed a residual metal resources method and designed a formula to assess mineralization intensity based on the results of stream sediment survey data. Xie et al. (2004) proposed the concept and methodology of large geochemical blocks that are the net results of Earth's original heterogeneity and of the distribution and redistribution of metals during its evolution. This method includes calculation formulae for determining metal endowments, geochemical block mineralization coefficients, and resources/potential resources, with this approach adopted in different parts of China and for different minerals (Liu and Xie, 2005).

In summary, different methods for estimating mineral resources in terms of geochemical data have been established in the past, but these have some deficiencies. Key points are as follows:

  • 1)

    Minimum targeting area: in the past, there was no specific requirement for the classification of minimum targeting areas, especially in terms of area size and interpolation parameters for spatial data, which need to be determined multiple times (Gong et al., 2013). The minimum targeting area is not only different from geometrical morphology, but in practice also needs to combine the geological background, ore-forming mechanisms, and the coexistence of many elements.

  • 2)

    Denudation parameter: There are many methods for resource estimation based on the principle of analogy. In calculating an endowment, the depth of the ore body is often considered, and the majority of depths of predicted ore bodies are estimated by referring to known model deposits of the same mineralization type. However, in the case of large deposits, there is a need to fully account for the extent of denudation of the ore deposit. According to the theory of primary halo zoning (Liu and Ma, 2007), due to differences in element activity and in precipitation temperature, a front halo is often formed around the ore body at low temperature, a near-ore halo is formed at medium temperature, and a tail halo is formed under high temperature conditions. If multiple phase superposition is not considered, the higher the intensity of the tail halo elements, the deeper the extent of denudation; in turn, the higher the intensity of front halo elements, the shallower the denudation. Using a combination of stream sediment elements, it is possible to distinguish the degree of denudation of the deposit (Zheng et al., 2014).

  • 3)

    Deposit positioning in prospective areas: mineral deposit density and target counting are two common methods used for estimating the numbers of deposits in the past (Cox, 1993, Mamuse et al., 2010, Singer, 1993). However, previous research seldom positioned deposits within a small area and seldom calculated endowments within a specific predicted deposit.

Based on existing research and on these limitations, we propose using regional geochemical data to estimate resources within a specific type of deposit. This paper discusses calculation methods, proposing important parameters for computation of resources and deducing corresponding equations for their calculation.

Section snippets

Minimum unit cell

As noted above, the earliest method used for resource estimation in China was based on the geochemical block (Xie et al., 2004). The geochemical block is ≥ 1000 km2. In subsequent studies of other proposed anomalous blocks, the geochemical block was reduced to small blocks of local geochemical anomalies, with areas of several km to tens of km.

This study uses rasterized geochemical images to create geochemical image sets, with the size of image pixel values being the area of the minimum unit cell.

Geological background of mineralization in the study area

The study area is located in China, at the junction with Russia, and Mongolia (Fig. 1). In terms of regional tectonics, the area is located on the south-eastern margin of the Siberian craton and has characteristics of an accreted continental margin (She et al., 2009). The NE–SW trending fault zone controls Mesozoic magmatic rocks and distribution of metal ore deposits. Moving from northeast to southwest, one finds in turn the Badaguan, Babayi, and Wunugetushan Cu–Mo deposits, the Jiawula and

Spatial mapping method

Most of the processes described below were carried out using ENVI software, except for the calculation of the regression equation.

Resource estimation

After the various coefficients were calculated, they could be compared to the deposits in the Manchuria area. The copper resources of the Wunugetushan copper molybdenum deposit total 2,232,000 tonnes, those of the Badaguan deposit 140,000 tonnes, and those of the Babayi deposit 61,000 tonnes. These resources are not generated within a single pixel; it is therefore necessary to consider which pixels are able to provide these resources. The search for these pixels is conducted by comparing the

Conclusions

In this study, geochemical data for the Manzhouli area obtained at the 1:200,000 scale were used to identify prospective target areas and to estimate the amount of copper ore resources. Resource estimation was based on a model deposit; i.e., through the study of a typical deposit, we determined the ore-bearing hydrothermal alteration, denudation, and surface mineralization intensity coefficients, also calculating the formation of resources. The following are the key findings:

  • 1.

    In this study,

Conflict of interest statement

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, this manuscript.

Acknowledgements

This research was supported by ‘pilot projects on ore-prospecting and exploration technique method in Daxinganling metallogenic belt’ of the Extensive Land Resources Survey programme (1212010781026). Prof. Keyan Xiao and Prof. Tianzhu Ye provided substantial help in the drafting of this paper. We also benefited from discussions with Jianbo Shao, researcher at the Shenyang Institute of Geology and Mineral Resources.

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