Spatial Overlay Analysis of Geochemical Singularity Index α-Value of Porphyry Cu Deposit in Gangdese Metallogenic Belt, Tibet, Western China
Source abstract
Abstract
The statistical modeling with ILR-RPCA-back CLR has two problems when dealing with the closure effect of geochemical data. Firstly, after performing isometric logratio (ilr) transformation, robust principal component analysis (RPCA) is employed for processing. The double-plot diagram illustrates that the element sequence transformation occurs in the first and second principal components, while the unique principal component remains unattainable. Secondly, by transforming both the score and load into the centered logratio (CLR) space using the U matrix, it is possible to obtain a score result that corresponds to the original order of elements according to the CLR = ILR·U formula. However, for obtaining a load result that corresponds to the original order of elements, an alternative formula “CLR = UT·ILR” must be used instead. In order to determine the optimal element assemblage for porphyry copper deposits, this study conducted statistical analysis on mineral assemblages from discovered deposits in the Gangdese metallogenic belt and identified Cu, Mo, Au, Ag, W, and Bi as key elements associated with porphyry copper deposits. Subsequently, by analyzing the singularities of the composite elements, the spatial overlay of the combined element is carried out, and concentration-area (C-A) fractal filtering is applied to identify the anomaly and background areas. To facilitate comparison, we conducted an analysis of various mineral and ore deposit types, revealing the following findings: (1) Combination elements exhibit superior recognition capability than single elements in porphyry copper deposits; (2) Skarn-type copper deposits unrelated to porphyry show a high degree of dissimilarity compared to those related to porphyry; (3) this method offers advantages over the single element method in evaluating porphyry gold deposits by reducing anomaly levels and initial investment during the evaluation stage for porphyry copper anomalies; (4) However, this method has limited ability in distinguishing between porphyry copper and molybdenum deposits.
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Research context
OpenAlex topics: Geochemistry and Geologic Mapping · Remote-Sensing Image Classification · Heavy metals in environment
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Earth Resources, China University of Geosciences, Wuhan 430074, China
Collaborative Innovation Center for Exploration of Strategic Mineral Resources, School of Earth Resources, China University of Geosciences, Wuhan 430074, China
Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China
Funding
National Natural Science Foundation of China
References (43)
Show source-provided references
1. Rui, Z., Huang, C., Qi, G., Xu, J., and Zhang, H. (1984). Porphyry Copper (Molybdenum) Deposits of China, Geological Phblishing House.
2. Wang, Z.T., Qin, K.Z., and Zhang, S.L. (1994). Geology and Exploration of Large Copper Deposits [Z], Metallurgical Industry Press.
3. Paleozoic epithermal Au and porphyry Cu deposits in North Xinjiang, China: Epochs, features, tectonic linkage and exploration significance10.1111/j.1751-3928.2002.tb00140.x ↗
4. Giant Porphyry Deposits: Characteristics, Distribution, and Tectonic Controls10.2113/gsecongeo.100.5.801 ↗
5. Finding, Characteristics and Significances of Qulong Superlarge Porphyry Copper (Molybdenum) Deposit
6. Singularity Modeling of Geo-Anomalies and Recognition of Anomalies Caused by Buried Sources
7. Xie, Y.S. (2004). The Study of the Complexity of Tectonic\Fluid\Mineralization System and its Dynamic Mechanism: A Case Study of Shuikoushan Pb\Zn\Au Polymetallic ore Field in Hunan Province, China, Guangzhou Institute of Geochemistry Chinese Academy of Science.
8. Robust factor analysis for compositional data10.1016/j.cageo.2008.12.005 ↗
9. A study of the lake sediment geochemistry of the Melville Peninsula using multivariate methods: Applications for predictive geological mapping10.1016/j.gexplo.2013.07.013 ↗
10. Analysis and mapping of soil geochemical anomalies: Implications for bedrock mapping and gold exploration in Giyani area, South Africa10.1016/j.gexplo.2014.11.018 ↗
11. A comparative study of trend surface analysis and spectrum–area multifractal model to identify geochemical anomalies10.1016/j.gexplo.2015.04.013 ↗
12. Fractal/multifractal modeling of geochemical data: A review10.1016/j.gexplo.2015.04.010 ↗
13. Identifying potential Au-Pb-Ag mineralization in SE Shuangkoushan, North Qaidam, Western China: Combined log-ratio approach and singularity mapping10.1016/j.gexplo.2017.04.001 ↗
14. Wang, H. (2013). The Effects of Compositional Data Closure Problem on Geochemical Data Analysis. [Master’s Thesis, China University of Geosciences]. (In Chinese).
15. The recognition and extraction of Au, Cu geochemical composite anomalies: A case study of the east of Laji Mountains
16. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China-ScienceDirect10.1016/j.oregeorev.2006.10.002 ↗
17. Mapping geochemical singularity using multifractal analysis: Application to anomaly definition on stream sediments data from Funin Sheet, Yunnan, China10.1016/j.gexplo.2009.09.002 ↗
18. Application of local singularity model to delineate geochemical anomalies in Xiong’ershan gold and molybdenum ore district, Western Henan province, China10.1016/j.gexplo.2010.06.001 ↗
19. Multifractals and geostatistics10.1016/j.gexplo.2012.04.001 ↗
20. Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas10.1016/j.gexplo.2012.07.007 ↗
21. Geochemical Evaluation of Exploration Prospect in the Xiongxcun Copper-Gold District and Peripheral Areas, Xietongmen County, Tibet10.1016/j.gexplo.2013.08.006 ↗
22. Identification of weak anomalies: A multifractal perspective10.1016/j.gexplo.2014.05.005 ↗
23. Spatial analysis and visualization of exploration geochemical data10.1016/j.earscirev.2016.04.006 ↗
24. Copper-Iron Polymetal Metallogenesis and Exploration Direction in the Western of Gangdese Metallogenic Belt, Tibet
25. Spatial-temporal framework of the Gangdese Orogenic Belt and its evolution
26. The varimax criterion for analytic rotation in factor analysis
27. Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt, Tibet, China10.1016/j.apgeochem.2011.03.122 ↗
28. Geochemical probe into China’s continental crust
29. Multifractal and geostatistic methods forcharacterizing local structure and singularityproperties of exploration geochemical anomalies
30. Spatial and scaling modelling for geochemical anomaly separation10.1016/S0375-6742(99)00028-X ↗
31. Identifying geochemical anomalies associated with Cu and Pb-Zn skarn mineralization using principal component analysis and spectrum-area fractal modeling in the Gangdese Belt, Tibet (China)10.1016/j.gexplo.2011.06.012 ↗
32. Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization10.1016/j.apgeochem.2012.10.031 ↗
33. The Separation of Geochemical Anomalies from Background by Fractal Methods10.1016/0375-6742(94)90013-2 ↗
34. Carranza, E.J.M. (2008). Geochemical Anomaly and Mineral Prospectivity Mapping in GIS, Elsevier.
35. Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China10.1016/j.oregeorev.2015.12.005 ↗
36. Geochemical evaluation of exploration prospect in the Xiongcun copper-gold district and peripheral areas, Xietongmen County, Tibet
37. Genesis of Luobuzhen Pb–Zn veins: Implications for porphyry Cu systems and exploration targeting at Luobuzhen-Dongshibu in western Gangdese belt, southern Tibet10.1016/j.oregeorev.2016.11.016 ↗
38. Geochemical Evaluation of Potential Ore in the Qulong Copper Deposit and Its Peripheral Region in Tibet
39. Zheng, W.B. (2012). The Study on Metallogenic Model and Prospecting Pattern for Jiama Polymetallic Copper Deposit, Tibet, Chengdu University of Technology. (In Chinese).
40. Regional metallogenic prognosis and mineral reserves estimation for porphyry copper deposits in Gangdese polymetallic ore belt, Tibet
41. Synthetic information prediction of porphyry copper in Tibet
42. Based on mineral resource assessment system (MRAS) for the metallogenic prognosis in Gangdese metallogenic belt, Tibet
43. Identifying geochemical anomalies associated with Sb–Au–Pb–Zn–Ag mineralization in North Himalaya, southern Tibet10.1016/j.oregeorev.2015.10.020 ↗