Numerical Simulation Based Targeting of the Magushan Skarn Cu–Mo Deposit, Middle-Lower Yangtze Metallogenic Belt, China

Taofa Zhou,Wenqiang Dai,Xiaohui Li,Feng Yuan,Rui Ye,Yue Li,Simon M. Jowitt,Xunyu Hu,Alison Ord

doi:10.3390/min9100588

Abstract

The Magushan Cu–Mo deposit is a skarn deposit within the Nanling–Xuancheng mining district of the Middle-Lower Yangtze River Metallogenic Belt (MLYRMB), China. This study presents the results of a new numerical simulation that models the ore-forming processes that generated the Magushan deposit and enables the identification of unexplored areas that have significant exploration potential under areas covered by thick sedimentary sequences that cannot be easily explored using traditional methods. This study outlines the practical value of numerical simulation in determining the processes that operate during mineral deposit formation and how this knowledge can be used to enhance exploration targeting in areas of known mineralization. Our simulation also links multiple subdisciplines such as heat transfer, pressure, fluid flow, chemical reactions, and material migration. Our simulation allows the modeling of the formation and distribution of garnet, a gangue mineral commonly found within skarn deposits (including within the Magushan deposit). The modeled distribution of garnet matches the distribution of known mineralization as well as delineating areas that may well contain high garnet abundances within and around a concealed intrusion, indicating this area should be considered a prospective target during future mineral exploration. Overall, our study indicates that this type of numerical simulation-based approach to prospectivity modeling is both effective and economical and should be considered an additional tool for future mineral exploration to reduce exploration risks when targeting mineralization in areas with thick and unprospective sedimentary cover sequences.

Highlights

Skarn deposits are widely distributed in both space and time, and represent important sources of copper, lead, zinc, molybdenum, and other metals [1,2,3]
Typical three-dimensional prospectivity modeling workflows frequently encounter issues of conditional dependence, where datasets are biased by relationships where given exploration criteria can generate responses in different datasets, particular mineralizing processes can generate more than one exploration criteria, or responses present within one dataset can be conditioned by responses in another dataset [12]
We address the following questions using new numerical simulations combined with the results of previous geological and geophysical research, namely: (1) Can numerical simulations enable the identification or verification of existing geological data?; (2) Can numerical simulations be used to identify prospective areas for exploration targeting in areas with thick and unprospective sedimentary cover sequences, such as those around the known Magushan deposit, and provide a guide for future mineral exploration? Both of these points are assessed during this study, demonstrating that numerical simulation can be used in both fundamental geological and economic geology research as well as in exploration targeting in regions with thick and unprospective cover sequences

Summary

Introduction

Skarn deposits are widely distributed in both space and time, and represent important sources of copper, lead, zinc, molybdenum, and other metals [1,2,3]. Skarns are dominated by calc-silicate minerals such as garnet and pyroxene, and the presences of these minerals are key in terms of the definition and identification of skarns [4]. Cu–Mo deposit is a skarn deposit in the southeast of the Nanling–Xuancheng area, one of the eight mining camps within the MLYRMB [8,9,10,11]. This area is covered by a thick and barren sedimentary cover sequence with little exposure of prospective lithologies, making traditional (e.g., geochemical, geological mapping, some geophysics) exploration difficult. Typical three-dimensional prospectivity modeling workflows (e.g., weights-of-evidence approaches) frequently encounter issues of conditional dependence, where datasets are biased by relationships where given exploration criteria can generate responses in different datasets, particular mineralizing processes can generate more than one exploration criteria, or responses present within one dataset can be conditioned by responses in another dataset [12]

Results

Discussion

Conclusion