Elsevier

Ore Geology Reviews

Volume 89, October 2017, Pages 1-14
Ore Geology Reviews

Prospectivity mapping for “Zhuxi-type” copper-tungsten polymetallic deposits in the Jingdezhen region of Jiangxi Province, South China

https://doi.org/10.1016/j.oregeorev.2017.05.022Get rights and content

Highlights

  • Zhuxi Cu-W polymetallic deposit is the world’s largest tungsten deposit.

  • Recognition criteria were established based on ore deposit model.

  • The singularity method is a powerful method to delineate geochemical anomalies.

  • The derivative norm method extracts magnetic gradient anomalies successfully.

  • WofE modelling gave a relative estimation for mineralization potential.

Abstract

The Zhuxi deposit is the largest copper-tungsten polymetallic deposit in the world and is in Jiangxi Province in South China. The ore body is characterized by hydrothermal-vein deposits of copper, lead, and zinc minerals at shallow levels, skarn deposits of tungsten and copper minerals at middle levels, and altered-granite-hosted copper and tungsten minerals at depth. Such metallogenic systems are typically intrusion-related. The intrusive granites related to the Zhuxi polymetallic deposit have been dated at 152.9 Ma to 146.9 Ma. The intrusions provided the thermal energy and the source material for the ore mineralization. Skarns mineralization, the main type of ore mineralization, developed in the contact zone of Carboniferous-Permian formations with the granites. Nappe structures changed the dip of the ore bodies from steep in the top part to gentle in the bottom. NE-trending faults provided the fluid pathways and controlled the geological framework and distribution of ore deposits on a regional scale. In this study, recognition exploration criteria were analyzed based on a mineral deposit model and the geological setting. Extraction of favorable geological information and GIS-based data-integration methods were used for mineral-prospectivity mapping of Zhuxi-type polymetallic deposits. Buffering analysis was employed to extract structural information (e.g. faults) and lithologic or stratigraphic information (e.g. granites or geologic units). The singularity method and spatially weighted principal component analysis were used to enhance and delineate geochemical anomalies. The derivative norm was utilized to extract magnetic-gradient anomalies associated with intrusive granites. Student t-test of weights-of-evidence (WofE) proved to be an effective way to optimize threshold values for binarization of variables as evidence layers by evaluating the spatial correlation between known deposits and geological variables. The posterior probabilities of WofE gave a relative estimation of mineralization potential. Areas delineated by high posterior probability had much higher potentiality for the discovery of new deposits where had none had been found yet.

Introduction

Jiangxi Province is an important tungsten province in China as well as in the world. Previous studies concluded that world-class tungsten orebodies (e.g. Xihuashan, Dajishan, Yaogangxian deposits) developed in the southern part of Jiangxi Province, within the Nanling tungsten-tin metallogenic belt (Li et al., 1986, Liu et al., 2014a, Mao et al., 2007, Peng et al., 2006). The northern part of Jiangxi Province, which adjoins the middle-lower Yangtze River Valley area, is well known for copper–gold–molybdenum–iron porphyry and skarn ore bodies. With the development of Chinese geological and mineral resources prospecting in recent years, two very large tungsten deposits, Zhuxi and Dahutang, were discovered in the northern Jiangxi Province. The discovery of these world-class tungsten deposits subverted the long-held spatial distribution pattern of “tungsten in the south and copper in the north” within Jiangxi Province and resulted in the establishment of the “North Yangtze tungsten belt” (Mao et al., 2012).

Mineral-prospectivity mapping is based on a mineral predictive model rather than on empirical data from mapping mineral deposits (Asadi et al., 2015). The model analyzes relevant recognition criteria for mineral deposits and synthesizes favorable evidence layers from multi-source data at a given scale (Bonham-Carter, 1994, Carranza, 2009, Carranza and Laborte, 2014). In the past few decades, numerous methods have been proposed and used in mineral-prospectivity mapping, which can be grouped into two classes: knowledge-driven and data-driven mineral predictive models. The knowledge-driven model uses the expert knowledge of mineral-deposit exploration to estimate metallogenetic potentiality in a given geological setting (Abedi et al., 2013, Carranza, 2008, Rodriguez-Galiano et al., 2015). It is suitable for green field modelling, which predicts mineral deposits where few or no orebodies are known to exist (Carranza and Laborte, 2014, Lusty et al., 2012). In contrast, the data-driven model is used in brownfield exploration, which delineates new targets for further exploration based on existing data for areas where orebodies are already known to exist. Weighted parameters are assigned to individual evidence layers to quantify the spatial association between known mineral deposits and geological features. The weights-of-evidence (WofE) model is a quantitative data-driven model based on the log-linear form of the Bayesian probability model to quantify spatial association (Agterberg and Cheng, 2002, Bonham-Carter, 1994, Cheng, 2008). It allows the users to calculate WofE layers and apply posterior probability mapping to mineral exploration.

Data from geochemical surveys and geophysical exploration were mapped with the geology of the Jingdezhen region (northern Jiangxi Province) at a scale of 1:50, 000 scale, and the mineral deposit model was run. In the study area, the resulting discovery of the intrusion-related Zhuxi copper-tungsten polymetallic deposit inspired a new guideline for mineral exploration. In this paper, we review the Zhuxi mineral deposit model and the orebody’s geological setting, analyze recognition criteria for regional exploration based on the mineral predictive model instead of the mineral deposit model, calculate posterior probability and delineate prospectivity targets by the WofE model.

Section snippets

Geological setting and study area

In terms of geological structure, South China consists of the Yangtze Craton in the northwest and the Cathaysia Block in the southeast. These two parts were assembled by a subduction-collision event at ca. 970 Ma (Li and Mcculloch, 1996). The event also resulted in the formation of Jiangnan Orogen between the Yangtze Craton and the Cathaysia Block (Wang et al., 2012). The approximately 1500-km long E-NE-trending Jiangnan Orogen consists of Precambrian meta-sedimentary sequences and igneous rocks

Mineral deposit model

The Zhuxi orefield is the most studied of any within the TFJD. When it was discovered, it was regarded as a reworked and overprinted sedimentary copper deposit. With the discovery of the underlying ore-bearing intrusive rocks, the Zhuxi orebody became the largest known tungsten deposit in the world. It was reported to contain 2.86 Mt WO3, 224.4 Kt Cu, and 1.2 Kt Ag (Li and He, 2016). The Zhuxi’s mineral reserve of WO3 is 2.7 times that of the Dahutang tungsten deposit, which previously was

Datasets

Geological mapping at 1:50, 000 scale, stream-sediment geochemical sampling, and geophysical surveying were carried out by Jiangxi Bureau of Geology and Mineral Exploration. For geochemical analysis, 7403 stream-sediment samples were analyzed for Au, Ag, Cu, Pb, Zn, W, Sn, Mo, Bi, As, Sb, Cr, Co Cd, Hg, Ni, and Ba. The ground magnetic survey was carried out along profiles with 500 m spacing and 100 m reading intervals. A total of 31,126 stations were included. These datasets were registered to

Preparation for creating evidence layers

Mineral-prospectivity mapping is used to delineate targets potentially containing ore mineralization based on spatial correlation of geological features with known ore deposits (Fallon et al., 2010). Mineral-exploration modelling was used to analyze spatial signatures between known deposits and geological features to establish recognition criteria on a regional scale. Some recognition criteria could be observed directly, whereas some were hidden in the data. In this section, methods of

Results and discussion

The input evidence layers for WofE for integrated prospectivity mapping were extracted based on recognition criteria shown in Table 1. The cell size was usually determined based on the geological complexity, research level, and data scale. In this study, the cell size, based on the data scale, was set to be 500 × 500 m. The posterior probabilities were calculated by GeoDAS software (Cheng, 2000). The cumulative proportions of ore deposits and area (Fig. 14), delineated by posterior probability

Conclusions

The well-known copper-tungsten mineralization is in a Neopaleozoic shallow marine carbonate-rock basin in the northeastern part of Jiangnan Orogen in Jiangxi Province in South China. The Zhuxi deposit is a typical deposit and the world’s largest tungsten deposit in the study area. It comprises two main ore zones and some scattered small orebodies, consisting of skarn-type, altered-granite type, and hydrothermal-vein type deposits. The mineralization has a significant zonation and is dominated

Acknowledgments

The authors sincerely thank Dr. Franco Pirajno and three anonymous reviewers for their critical reviews and constructive comments which have improved the manuscript. This research has been financially supported by Chinese Geological Survey Program (12120113065300), Welfare Research Program of Ministry of Land and Resources, PRC (201411035), National Key Technology R&D Program (No. 2011BAB06B08-2) and National Training Program of Innovation and Entrepreneurship for Undergraduates (201610491027).

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