Spatial Analysis And Quantitative Assessment Of Geochemical Anomalies Caused By Ore Bodies

Geochemical exploration as an important prospecting method has a strong position in mineral exploration, which is very suitable to explore precious metals (gold, silver) and non-ferrous metals (copper, lead, zinc and so on). In geochemical exploration activities, recognition and evaluation of geochemical anomalies caused by ore bodies and discrimination of ore sources are both critical. When a kind of elements (isotopes) is riched or depleted in sampling media, it is called geochemical anomalies that can be devided into positive anomalies and negative anomalies. Since ore-forming elements are a gradual process of enrichment, geochemical anomalies caused by ore bodies are usually positive anomalies and the main evaluation object. In the case of recognition and evaluation of geochemical anomalies caused by ore bodies, its spatial analysis and quantitative evaluation are two cutting edge problems.In recent years, theme data from different disciplines are sharply increasing and database technology has been greatly improved, thus the traditional data analysis has become an independent interdisciplinary called data mine or data science. The purpose of data mine is to discover professional knowledges, so data gold can be refined from data factory. When exploration geochemists analyze and evaluate geochemical data, meanwhile they are also playling the role of data scientists or data mining engineers. That is, through analysis of high-dimensional geochemical data derived from two or more sources, prospecting knowledges can be obtained.Based on above researth background, this thesis regards modern statistics, robust statistics, compositional data analysis and spatial analysis theory as guides. Both GIS software and R contributed packages are used for univariate and multivariate statistical analysis. Finally the researth will achieve two goals; one is proposing new quantitative methods, the other is prospecting knowledges served as exploration decision-making. According to the research objectives, the researth contents mainly include as follows:(a) Insight and summary of several inherent properties of exploration geochemical data, especially theoretical analysis and empirical study of outlier, closure, and spatial autocorrelation;(b) A solution of geochemical data check and summary of various data transformations; discussion of the superiority of three kinds of logratio data transformation;(c) Exploratory data analysis of single element and multielement, concerning statistical and spatial distribution of single element, and graphic visualization of multielement;(d) Contrastive analysis of geochemical anomaly threshold calculated by four different ways, implementation of automated multielement outlier detection and visualization, analysis of a new source identification method about geochemical anomalies caused by ore bodies, both PCA and FA used to trace spatial process of copper and tungsten (metallogenesis);(e) data-driven quantitative methods such as cluster analysis, discriminant analysis, and regression analysis, as well as a novel knowledge-driven quantitative method called similarity method.This dissertation selects the Jiurui copper and Dahutang tungsten as two study cases. There are different data types among research area, namely1081composite samples of regional geochemical data in Jiurui ore field,3142composite samples of regional geochemical data in Dahutang ore field,6weathered soil profiles and6516chemical samples in No.4exploration section located at Shimensi ore mine. These data have distinctive multimedia (rock, soil, and stream sediment), multielement (up to39elements), and multiscale (regional and local area).Based on fully understanding geological and metallogenic characteristics of Jiurui and Dahutang ore field, then making further efforts to analyze and evaluate Cu and W geochemical anomalies caused by ore bodies, ultimately, the eventual research findings of six aspects are generalized as below:1) Ten salient features of exploration geochemical data have been summarized in detail for the first time, which are nonnegativity, continuity, batchwise, heterogeneity, autocorrelation, scale invariance, superposition (mixing), truncation (censored), outlier, and closure. And besides, five alternatives of geochemical data check have been proposed and five significant methods of data transformation have been summarized. Moreover, three kinds of logratio data transformations are the best solution to solve closure problem about compositional data.2) Combined with some basic characteristics of geochemical data, a integral solution is provided about exploratory data analysis of single element and multielement respectively.(a) For exploratory data analysis of single element, four kinds of methods are used to explore statistical distribution and spatial distribution, which are graphic method, skewness and kurtosis, Kolmogorov-Smirnov and Shapiro-Wilk normal test, and C-A fractal method. Their strengths and weaknesses are analyzed. The intuitively graphic method includes the one-dimensional scatterplot, the histogram, the density trace, plot of the empirical cumulative distribution function (CDF-plot), the quantile-quantile plot (QQ-plot), the cumulative probability plot (CP-plot), the probability-probability plot (PP-plot), and boxplots. The histogram, CP-plot, and Tukey boxplot have good performance in outlier and censored data detection, so they are very popular in geochemical domain. In addition, rational division of the block number is crucial in histogram. So, in order to comprehensively inspect data structure and behavior, it is recommended to combine multiple diagrams.(b) Except exploratory data analysis of single element, there are four multivariate graphics can be used to reveal the interrelation of multielement, whose names are called as correlogram, parallel coordinates plot, spatial trend plot, and spatial distance plot. The function of correlogram is used to display correlation coefficient matrix. Comparison of Pearson correlation coefficient and robust correlation coefficient, it has been shown that the former is larger than the later. Futher, when colours and icons are used to denote the size of correlation coefficients, it is very efficient and convenient to compare correlation coefficients. The parallel coordinates plot is a graphical representation of all profiles of all observations in just one plot, thus it is a powerful tool for multielement analysis. The spatial trend plot is designed to study the distribution of all the measured variables along a transect line via selecting the x-(for the east-west-transect) or y-coordinate (for the north-south-transect) for the x-axis of the plot and any other variable for the y-axis and plotting these as xy-plots. Whilst the spatial distance plot is designed to study systematic changes of multielement with distance from a defined point in all directions and irregular region.That is, these two plots provide a new idea for spatial analysis and quantitative evaluation of geochemical anomalies caused by ore bodies.3) One of the essential prerequisites of geochemical anomaly recognition is to calculate a threshold between background and anomaly. The geochemical threshold is a critical thing for single element, which can be calculated by four formulas. The first formula is MEAN±2SD, which is based on classical statistics. The second formula is MEDIAN±2MAD, which is based on robust statistics. The third formula is Q3+1.5·IQR, which is based on Tukey boxplot. The last formula is uppermost2percent of the data defined as "outliers" for further inspection, which is based on percentiles. Compared with four results, findings demonstrate that the first formula is not appropriate for ore-forming elements and yet the second and the third formula are recommended because of their robustness. When one geochemist employs the second and the third formula to calculate geochemical threshold, it is better to transform original data by log10or logit. The problem of the last formula is that even percentiles will change with size or location of the survey area.In addition to geochemical anomaly of single element, the algorithm of automated multielement outlier detection and visualization has been realized. Taking two element associations represented Chengmenshan copper deposit (Cu+Mo+Au+Ag+W+Sb+Zn) and Shimensi tungsten deposit (W+Cu+F+Ag+As) for example, the interpretation of multielement outliers is a challenge, but maps and graphics can help to understand the patterns of multielement outliers.4) Based on recognition of geochemical anomaly caused by ore bodies for Cu and W, the sources of Cu and W geochemical anomlies caused by ore bodies have been identificated via multiscale and multimedia geochemical data. It shows that:(a) The ore forming material of Cu anomalies is derived from deep source related with intermediate-acid magmatic rock, and conversely the ore forming material of W anomalies is erived from shallow source related with acidic granite.(b) Using multiscale analysis (from regional area to local area) and principle of element classification, every typical deposit has been summarized a suite of element association. The element association of Chengmenshan copper deposit is Cu+Mo+Au+Ag+W+Sb+Zn; the element association of Wushan copper deposit is Cu+Au+Ag+Pb+As+Sb; the element association of Zengjialong tin deposit is Sn+F+As+Sb; the element association of Shimensi tungsten deposit is W+Cu+F+Ag+As; the element association of Shiweidong tungsten deposit is W+Cu+F+Ag+As+Sb; the element association of Xianglushan tungsten deposit is W+Cu+F+Ag. These element associations can be regarded as prospecting criteria in regional geochemical exploration.(c) From the point of view of element concentration, in the case of six weathered soil profiles in Dahutang ore field, the concentration of ore forming elements appears to increase slowly in vertical direction, so B-horizon soils are the best sampling horizon. Further more, taking No.4exploration section located at Shimensi ore mine for example, two main ore forming elements have been analyzed in different concentration intervals and rock types. Both copper ore body and tungsten ore body have two significant concentration centres and their bonanzas are overlapped especially in hydrothermal breccia and quartz vein bearing tungsten. Afterward Fry plot has been applied to reveal migration direction and dip angle of the trajectory of W and Cu. The results show that their migration directions are from north-east to south-west and their dip angles are25°～30°and30°respectively.(d) Seven indicator elements (Cu+Mo+Au+Ag+W+Sb+Zn) selected in Jiurui ore field and six indicator elements (W+Cu+F+Ag+As+Sb) selected in Dahutang ore field have been carried out principal component analysis (abbreviated to PCA) and factor analysis (abbreviated to FA). It has been realized that biplots and scores map are useful to trace minerogenesis process of Cu and W. Because geochemical compositional data are affected by outliers and constrained by closure, it is better to transform original data via centered logratio tansformations (abbreviated to clr) prior to statistic analysis. As far as interpretability of statistical results, the interpretability of PCA is higher than the interpretability of FA.5) Three data-driven methods have been studied, which is aimed at quantitative evaluation of Cu and W geochemical anomalies caused by ore bodies in Jiurui and Dahutang ore field. These three methods are cluster analysis, discriminant analysis and regression analysis. Cluster analysis can be used to optimize element association for regional geochemical data. Discriminant analysis is effective to classify observations in three subareas called copper (Cu) mineralized area, tin (Sn) mineralized area, and deep-sea facies sedimentary strata area, and the consequence is that error rate of classification in Sn mineralized area is the lowest. Six multiple linear regression equations have been established in six corresponding the nearest neighbour of Chengmenshan, Wuhan, Zengjialong, Shimensi, Shiweidong, and Xianglushan deposit. According to comprehensive analysis (including t test of regression coefficients, F test of regression equation, and adjusted R square), prediction accuracy of all six multiple linear regression equations is good. These conclusions can provide a strong foundation for next knowledge-driven analysis.6) A novel knowledge-driven method has been created to evaluate Cu and W geochemical anomalies caused by ore bodies in two ore fields named Jiurui and Dahutang, and its name is known as similarity coefficient method and its fuction is to identify similar mineralization type. Subsequently, the essential connotation of geochemical similarity coefficient is explained and five executive steps have been made. The first step is to select some typical deposits. The second step is to sieve element association. The third step is to make "standard sample". The fourth step is to calculate distance value and then to convert into similarity coefficient. The last step is to draw map of similarity coefficient. A few key details have been profoundly discussed, such as data transformations, concentration assignment of "standard sample", calculation of weight coefficient, distance formula of Euclidean and Canberra, classification scheme in symbol map of similarity coefficient, spatial interpolation parameter in contour map of similarity coefficient and spatial autocorrelation pattern of similarity coefficient. Knowledges have been acquired by trial and error and intensive comparison as below.(a) Logarithmic transformation is better than normalized transformation in Euclidean distance formula.(b) The concentration of indicator element is assigned arithmetical mean of geochemical anomaly and its weight is preferred to calculate by relative weights algorithm in "standard sample".(c) Improved Canberra distance formula is preferable to calculate similarity coefficient of two "standard samples" of copper deposit in Jiurui ore field, whilst improved Euclidean distance formula is more suitable to calculate similarity coefficient of three "standard samples" of tungsten deposit in Dahutang ore field.(d) When drawing symbol and contour map of similarity coefficient, for the purposes of comparison, classification scheme is adopted by percentile classes. Geochemists are often more interested in the tails of the distribution of similarity coefficient, so to highlight the tails the50th,90th,95th,98thpercentiles can be used.(e) Taking regional geochemical data for example, when drawing contour map of similarity coefficient in Jiurui and Dahutang ore fields, the optimum search radius is4.5km corresponding to maxium global Moran’s I. Five LISA cluster maps shows that High-High pattern and High-Low patern are next key exploration target areas.According to thoughts of integrated decision-making, next prospecting strategies of Jiurui and Dahutang ore fields are suggested as below.In Jiurui ore field, if Canberra similarity coefficient of observations calculated by Chengmenshan copper "standard sample" is not less than0.298and Canberra similarity coefficient of observations calculated by Wushan copper "standard sample" is not less than0.405, these observations can be regarded as the first consideration in next copper prospecting targets. However, if one of above two conditions is not satisfied, these observations can be regarded as the second choice in next copper prospecting targets. In Dahutang ore field, if three conditions are satisfied, which are Euclidean similarity coefficient of observations calculated by Shimensi tungsten "standard sample" is not less than0.602, Euclidean similarity coefficient of observations calculated by Shiweidong tungsten "standard sample" is not less than0.578and Euclidean similarity coefficient of observations calculated by Xianglushan tungsten "standard sample" is not less than0.717, these observations can be regarded as the first consideration in next tungsten prospecting targets. If one of above three conditions is not satisfied, these observations can be regarded as the second choice in next tungsten prospecting targets. If two of above three conditions is not satisfied, these observations can be regarded as the third choice in next tungsten prospecting targets.In conclusion, there are three inovations as follows:(a) There are two powerful spatial plots to be used to display the spatial relationship of multielement. The spatial trend plot is designed to study the distribution of all the measured variables along a transect line via selecting the x-(for the east-west-transect) or y-coordinate (for the north-south-transect) for the x-axis of the plot and any other variable for the y-axis and plotting these as xy-plots. Whilst the spatial distance plot is designed to study systematic changes of multielement with distance from a defined point in all directions and irregular region.That is, these two plots provide a new idea for spatial analysis and quantitative evaluation of geochemical anomalies caused by ore bodies.(b) A novel knowledge-driven method has been created to evaluate Cu and W geochemical anomalies caused by ore bodies in Jiurui and Dahutang ore field, whose name is known as similarity coefficient method and whose fuction is to identify similar mineralization type. Subsequently, the essential connotation of geochemical similarity coefficient is explained and five executive steps have been made. The first step is to select some typical deposits. The second step is to sieve element association. The third step is to make "standard sample". The fourth step is to calculate distance value and then to convert into similarity coefficient. The last step is to draw map of similarity coefficient.(c) A fresh idea is proposed that the sources of geochemical anomlies caused by ore bodies have been identificated via multiscale and multimedia geochemical data. The sources of Cu and W geochemical anomlies caused by ore bodies have been identificated and the minerogenesis process of Cu and W is traced by PCA and FA.