Qinglin Xia

Mineral Potential Mapping for Tungsten Polymetallic Deposits in the Nanling Metallogenic Belt, South China

The Nanling belt in South China has considerable resources of tungsten polymetallic commodities and is one of the most important metallogenic belts in the world. Data-driven weights-of-evidence (WofE) and fuzzy logic models are used to evaluate the tungsten polymetallic potential of the Nanling belt. Initially, seven ore-controlling factors derived from multi-source geospatial datasets (e.g., geological, geochemical, and geophysical) are used for data integration in the two models. Two mineral potential maps are generated that efficiently predicate the locations of the deposits. The WofE map predicate 81% of the deposits within 13.6% of the study area, whereas the fuzzy logic map predicate 81.5% of the deposits within 13% of the area. The predictive maps are syntheses of spatial association rules, which provide better understanding of those factors that control the distribution of mineralization and trigger eventual exploration work in new areas. Subsequently, in order to evaluate the success rate accuracy, the receiver operating characteristic curves and area under the curves (AUCs) for the two potential maps are constructed. The results show that the AUCs for the WofE and fuzzy logic models are 0.775 7 and 0.840 6, respectively. The higher AUC value for the fuzzy logic model implies that it delineate a greater number of favorable areas compared with the WofE model. Overall, the capabilities of both models for correctly classifying areas with existing mineral deposits are satisfactory.

Multivariate analysis of stream sediment data from Nanling metallogenic belt, South China

Multivariate statistical methods can be applied to analyse a complete set of multidimensional geochemical data and to identify latent relationships among these data. In this paper, we used multivariate statistical analysis, including k-means clustering, principal component analysis (PCA) and factor analysis (FA), to study the similarity of the sampling points and the relationships between metal mineralization and geological environment in the Nanling metallogenic belt, South China. The dataset consists of 1617 sediment samples analysed for 39 elements. The dataset was divided into three clusters by k-means clustering which were strongly associated with the distribution of lithostratigraphic units and the level of metal mineralization. Each cluster was analysed by PCA to identified principal components. Three factors extracted by the factor analysis explained c. 62% of the total variance and allow identification of the dominant ore-forming environment. Factor 1 describes c. 30% of the common variance and is highly loaded by Zn, Cu, Cr, Co, Ni, Mn, P, Ti, V, Mg and Fe. Factor 2 includes rare metals, rare earth elements (REE) and radioactive elements with Y, La, Nb, Zr and U, explaining c. 19% of the common variance. Factor 3 describes c. 14% of the common variance and is highly loaded by W, Sn, Mo, Pb, Be and Bi, representing tungsten polymetallic mineralization. In this paper, the Student’s t-test derived from weights-of-evidence modeling was used to measure the significance of spatial correlation between factor scores and mineral deposits.

Identification of REE mineralization-related geochemical anomalies using fractal/multifractal methods in the Nanling belt, South China

Singularity analysis in the multifractal context adds new tools to conventional approaches in dealing with geochemical data, which can be applied to characterize how the statistical behavior varies as measuring scale changes. Hybrid frequency distribution patterns can be detected by singularity analysis because of the regularity of enrichment and dispersion of geochemical elements in the Earth’s crust. In the present study, a case study of anomaly identification of REE mineralization-related La and Y concentration values from 1,617 stream sediment samples in the Nanling belt, South China, has been used to demonstrate the application of two-fractal/multifractal methods, singularity analysis and concentration–area (C-A) fractal method. First, singularity analysis was used to identify weak anomalies hidden within geochemical background for the prediction of the present of REE mineralization. And then, the C-A fractal method was applied to determine threshold values of singularity indices for separating anomalies from background. The results indicate that nonlinear models and methods related to fractal/multifractal (singularity analysis and C-A method) can provide powerful tools for the quantification of geochemical anomaly characteristics, and hybrid frequency distribution patterns can be identified by combining singularity analysis and C-A method due to different distribution patterns of background and anomaly of geochemical data.

Spatial patterns of geochemical elements measured on rock surfaces by portable X-ray fluorescence: application to hand specimens and rock outcrops

Spatial distribution of geochemical elements at hand-specimen and outcrop scale provides significant evidence for the processes of formation and alteration of rocks. Portable X-ray fluorescence (pXRF) analyses is a non-destructive and cost-effective methodology to rapidly measure multi-elemental concentrations in-situ, and its application makes the study of the distribution of geochemical elements efficient. In this paper two pXRF instruments, the Tianrui EDX-P730S and the Niton XL3t 950, were employed for measuring geochemical data for two samples of Pb and Zn ore hand specimens and at outcrop scale in an area of skarn formation. The data are processed by GIS and principal component analysis (PCA) for interpreting element associations involved in mineralization processes. The first three principal components obtained by PCA for the data measured on the rock surface of the two hand specimens may represent early stage Zn-dominated mineralization, superimposed Zn and Pb mineralization and late stage Pb-dominated mineralization. These three zones of mineralization can be delineated by the scores of multiple elements on the first three principal components. The first four principal components obtained from the outcrop surface data were found to be related to the marble, diorite, and two stages of skarn-type mineralization (Cu and Zn mineralization and Pb, Zn and Fe mineralization).

The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China

In this study, the evidential belief functions (EBFs) were applied for mapping tungsten polymetallic potential in the Nanling belt, South China. Seven evidential layers (e.g., geological, geochemical, and geophysical) related to tungsten polymetallic deposits were extracted from a multi-source geospatial database. The relationships between evidential layers and the target deposits were quantified using EBFs model. Four EBF maps (belief map, disbelief map, uncertainty map, and plausibility map) are generated by integrating seven evidential layers which provide meaningful interpretations for tungsten polymetallic potential. On the final predictive map, the study area was divided into three target zones of high potential, moderate potential, and low potential areas, among which high potential and moderate potential areas accounted for 17.8% of the total area, containing 81% of the total deposits. To evaluate the success rate accuracy, the receiver operating characteristic (ROC) curves and the area under the curves (AUC) for the belief map were calculated. The area under the curve is 0.81 which indicates that the capability for correctly classifying the areas with existing mineral deposits is satisfactory. The results of this study indicate that the EBFs were effectively used for mapping mineral potential and for managing uncertainties associated with evidential layers.

Developing a Geological Management Information System: National Important Mining Zone Database

ABSTRACT Geo-data is a foundation for the prediction and assessment of ore resources, so managing and making full use of those data, including geography database, geology database, mineral deposits database, aeromagnetics database, gravity database, geochemistry database and remote sensing database, is very significant. We developed national important mining zone database (NIMZDB) to manage 14 national important mining zone databases to support a new round prediction of ore deposit. We found that attention should be paid to the following issues: data accuracy: integrity, logic consistency, attribute, spatial and time accuracy; management of both attribute and spatial data in the same system; transforming data between MapGIS and ArcGIS; data sharing and security; data searches that can query both attribute and spatial data. Accuracy of input data is guaranteed and the search, analysis and translation of data between MapGIS and ArcGIS has been made convenient via the development of a checking data module and a managing data module based on MapGIS and ArcGIS. Using ArcSDE, we based data sharing on a client/server system, and attribute and spatial data are also managed in the same system.