Exploring the joint compositional variability of major components and trace elements in the Tellus soil geochemistry survey (Northern Ireland)

The complexity of modern geochemical data sets is increasing in several aspects (number of available samples, number of elements measured, number of matrices analysed, geological-environmental variability covered, etc), hence it is becoming increasingly necessary to apply statistical methods to elucidate their structure. This paper presents an exploratory analysis of one such complex data set, the Tellus geochemical soil survey of Northern Ireland (NI). This exploratory analysis is based on one of the most fundamental exploratory tools, principal component analysis (PCA) and its graphical representation as a biplot, albeit in several variations: the set of elements included (only major oxides vs. all observed elements), the prior transformation applied to the data (none, a standardization or a logratio transformation) and the way the covariance matrix between components is estimated (classical estimation vs. robust estimation). Results show that a log-ratio PCA (robust or classical) of all available elements is the most powerful exploratory setting, providing the following insights: the first two processes controlling the whole geochemical variation in NI soils are peat coverage and a contrast between “mafic” and “felsic” background lithologies; peat covered areas are detected as outliers by a robust analysis, and can be then filtered out if required for further modelling; and peat coverage intensity can be quantified with the %Br in the subcomposition (Br, Rb, Ni).     

结构方程模型在地球化学数据分析中的应用

为了实现通过确定地球化学组合元素来反映成矿异常, 本文在主成分分析模型的基础上, 引入了新的结构方程模型(SEM).与主成分所不同的是, 结构模型综合了经典统计方法中的因子分析和路径分析方法, 以与研究对象具有较好的拟合度为标准来确定最优解, 并通过模型最优解来确定新的成分组合, 因此结构模型所确定的成分变量不一定是具有最大变化性, 而是与研究对象最接近的因子变量, 该因子能够更好地反映研究对象.介绍了结构方程模型方法的原理, 并利用加拿大Nova Scotia省西南部湖泊沉积物地球化学数据建立了与热液型金矿有关的地球化学元素结构方程模型, 研究了结构方程模型所给出的组合变量空间分布规律以及与金矿床的关系.与主成分分析方法所给出的计算结果进行对比发现, 结构模型所计算的与金矿相关的组合变量与矿床的空间相关性较高, 并且对金矿床(矿点)也具有较好的预测性.      

The control of maturity and kerogen type on quantitative analytical pyrolysis data

Quantitative pyrolysis-gas chromatography has been performed on 96 kerogen samples isolated from 17 wells on the Norwegian Continental shelf. Petrographic and bulk geochemical measurements were also performed on the samples, and a combined data set of 117 variables for each sample was analysed using principal components analysis (PCA). This approach provides an objective and reproducible means of kerogen characterisation, which can be easily automated. In addition to objective kerogen characterisation and facile visualisation of facies and maturity related chemical trends, the method has the potential to allow objective prediction of key geochemical parameters such as maturity level from pyrogram data.     

Using robust staged R-mode factor analysis and logistic function to identify probable Cu-mineralization zones in Khusf 1:100,000 sheets,east of Iran

Factor analysis method is a multivariate analysis technique that is widely used for the interpretation of stream sediment geochemical data. The purpose of factor analysis is describing the changes in a set of multi-element geochemical data by reducing the dimension of the data and variables to a number of factors that can present the hidden association between elements. Differences in mobility, physical, and chemical properties of the elements and the nature of the factor analysis method in which the matrix of all data is used cause paragenes elements not to be found on the output of factor analysis. In this research, to improve the output of factor analysis for deriving the best reagent multi-element mineralization, robust staged factor analysis method was used according to the close nature of geochemical data in order to identify the Cu-mineralization potential in Khusf 1:100,000 sheets located at the east of Iran. The robust staged factor analysis enhances the recognition of anomalous geochemical signatures and increases geochemical anomaly intensity and the percentage of the total explained variability of data. As indicated by the results of the study, few anomalous zones have been found in the study area. The observation of chalcopyrite and malachite mineralization in andesite and dacite–andesite rocks in a region during the field study confirms the effectiveness of the robust SFA technique. Such studies can be used by mine engineers and geologists for designing an optimum grid exploration on the next exploration steps.     

Application of classical statistics,logratio transformation and multifractal approaches to delineate geochemical anomalies in the Zarshuran gold district,NW Iran

The aim of this study is to discriminate the geochemical anomalies in the Zarshuran district, NW Iran, using different geochemical methods and present a more useful method where anomalous areas better coincide with the geological features. For this methods of delineation, geochemical anomalies were compared using geological features, occupied area of anomalies respect to the total study area, and field observations. Frequency based analysis such as mean + 2SDEV and median + 2MAD and concentration–area (C–A) multifractal methods were adopted for estimating thresholds and separating geochemical anomalies in uni-element data, as well as multi-element ones. Threshold values obtained from mean + 2SDEV and median + 2MAD, from original point geochemical data, are smaller than those of the pixel values; this may be due to the stronger variance of pixel values. In addition, the C–A multifractal method, as a useful tool to identify weak geochemical anomalies, was applied for defining the threshold values. Robust principal component analysis (RPCA) methods coupled with isometric log-ratio (ilr) transformations were utilized to open the geochemical data in order to reduce the effects of the data closure problem. The 20-quantile intervals decomposed anomaly maps from PC1 were obtained from the classical PCA, robust PCA showed that the upper quintile (>80 quintile) of classical PCA covers a larger area (32.54%) than the robust PCA (18.16%), and as a result, the robust PCA displayed smaller areas and has good spatial associations with outcrops of hydrothermal Au–As mineralization in this area; coincident with the known Zarshuran former mining area (ore field), Zarshuran unit, Ghaldagh silicified limestone occurrence and newly explored works confirmed by field observation. Although the C–A model shows a smaller area (8.06%), this anomaly location is limited to the Zarshuran old mining area with no new exploration targets. Comparison of the models indicates that the RPCA model is not only beneficial to further Au exploration in the study area, but also provides a meaningful geological study to the community of the compositional data analysis.     

Multivariate statistical analysis of geochemical data of groundwater in Veeranam catchment area,Tamil Nadu

The study of hydrogeochemistry of the Mio-Pliocene sedimentary rock aquifer system in Veeranam catchment area produced a large geochemical dataset. Groundwater samples were collected at 52 sites over 963.86 km² area and analyzed for major ions. The large number of data can lead to difficulties in the integration, interpretation and representation of the results. Two multivariate statistical methods, Hierarchical cluster analysis (HCA) and Factor analysis (FA), were applied to a subgroup of the dataset to evaluate their usefulness to classify the groundwater samples, and to identify geochemical processes controlling groundwater geochemistry. Hydrochemical data for 52 groundwater samples were subjected to Q- and R- mode factor and cluster analysis. R-mode analysis reveals the inter-relations among the variables studied and the Q-mode analysis reveals the inter-relations among the samples studied. The R-mode factor analysis shows that Ca, Mg and Cl with HCO₃ account for most of the electrical conductivity, total dissolved solids and total hardness of groundwater. The ‘single dominance’ nature of the majority of the factors in the R-mode analysis indicates non-mixing or partial mixing of different types of groundwater. Both Q-mode factor and Q-mode cluster analyses indicate an exchange between the river water and the groundwater in the vicinity. The rock water interaction like flood basin back swamp deposits of silty clayey formation is the major cause for the cluster II classification. Cluster classification map reveals that 58% of the study area comes under cluster II classification.     

Stream sediment geochemical exploration for gold in Central Eastern Desert,Egypt: Application of the concentration-number fractal model,factor analysis,and geochemical mineralization probability index

Geochemical exploration by stream sediment sampling using bulk leach extractable gold (BLEG) technique and applying concentration-number (C-N) fractal model, factor analysis (FA), and geochemical mineralization probability index (GMPI) resulted in the recognition of new Au occurrences around the Sukari gold mine in the central Eastern Desert of Egypt. The geochemical data of 128 stream sediment samples collected from the study area was used for delineating the geochemical anomalies and characterizing the dispersion trains of ore and associated elements (Au, Ag, As, Sb, Cu, Pb, Zn, Mo). Statistical analysis of the geochemical data applying the C-N fractal modeling enabled us to identify significant anomaly and background populations of the investigated elements and to construct reliable geochemical anomaly maps. Factor analysis using centered log-ratios (CLR), to address the problem of closed compositional data, revealed significant element associations for mineralization (Au, As, Mo, Zn, Ba), country rock compositions (Rb, Li, Be, Sn, Bi for granite, and Co, Cr, Ni for mafic rocks), and element mobility (e.g. Sb, Zr, and Ag). Weak and moderate Au anomalies that cannot be detected by factor score maps can be delineated clearly by using the C-N fractal method and GMPI distribution map. Our study revealed that Ag, As, and Sb are the main pathfinder elements for gold mineralization in arid to semiarid regions exemplified by the Sukari gold district. Silver can be used as a “direct” pathfinder, whereas As and Sb are “indirect” pathfinders for Au in such regions. The spatial distribution of Au and Ag anomalies indicate that gold mineralization in the Sukari district is structurally controlled. However, the spatial distribution of Cu, Pb, Zn, and Mo is controlled by mineralogical and lithological factors and is not related to any significant base metal deposits.     

A machine learning approach for regional geochemical data:Platinum-group element geochemistry vs geodynamic settings of the North Atlantic Igneous Province

Whilst traditional approaches to geochemistry provide valuable insights into magmatic processes such as melting and element fractionation, by considering entire regional data sets on an objective basis using machine learning algorithms(MLAs), we can highlight new facets within the broader data structure and significantly enhance previous geochemical interpretations.The platinum-group element(PGE) budget of lavas in the North Atlantic Igneous Province(NAIP) has been shown to vary systematically according to age, geographic location and geodynamic environment.Given the large multi-element geochemical data set available for the region, MLAs were employed to explore the magmatic controls on these shifting concentrations.The key advantage of using machine learning in analysis is its ability to cluster samples across multi-dimensional(i.e., multi-element)space.The NAIP data set is manipulated using Principal Component Analysis(PCA) and t-Distributed Stochastic Neighbour Embedding(t-SNE) techniques to increase separability in the data alongside clustering using the k-means MLA.The new multi-element classification is compared to the original geographic classification to assess the performance of both approaches.The workflow provides a means for creating an objective high-dimensional investigation on a geochemical data set and particularly enhances the identification of metallogenic anomalies across the region.The techniques used highlight three distinct multi-element end-members which successfully capture the variability of the majority of elements included as input variables.These end-members are seen to fluctuate in prominence throughout the NAIP, which we propose reflects the changing geodynamic environment and melting source.Crucially, the variability of Pt and Pd are not reflected in MLA-based clustering trends, suggesting that they vary independently through controls not readily demonstrated by the NAIP major or trace element data structure(i.e., other proxies for magmatic differentiation).This data science approach thus highlights that PGE(here signalled by Pt/Pd ratio) may be used to identify otherwise localised or cryptic geochemical inputs from the subcontinental lithospheric mantle(SCLM) during the ascent of plume-derived magma, and thereby impact upon the resulting metallogenic basket.     

Assessment of water quality and identification of pollution sources of three lakes in Kashmir, India, using multivariate analysis

Multivariate statistical techniques, such as cluster analysis, principal component analysis (PCA) and factor analysis (FA) were applied to evaluate and interpret the water quality data set for 13 parameters at 10 different sites of the three lakes in Kashmir, India. Physicochemical parameters varied significantly (p?<?0.05) among the sampling sites. Hierarchical cluster analysis grouped 10 sampling sites into three clusters of less polluted, moderately polluted and highly polluted sites, based on similarity of water quality characteristics. FA/PCA applied to data sets resulted in three principal components accounting for a cumulative variance of 69.84, 65.05 and 71.76% for Anchar Lake, Khushalsar Lake and Dal Lake, respectively. Factor analysis obtained from principal components (PCs) indicated that factors responsible for accelerated eutrophication of the three lakes are domestic waste waters, agricultural runoff and to some extent catchment geology. This study assesses water quality of three lakes through multivariate statistical analysis of data sets for effective management of these lakes.     

Factor analysis applied to regional geochemical data: problems and possibilities

A large regional geochemical data set of C-horizon podzol samples from a 188,000 km² area in the European Arctic, analysed for more than 50 elements, was used to test the influence of different variants of factor analysis on the results extracted. Due to the nature of regional geochemical data (neither normal nor log-normal, strongly skewed, often multi-modal data distributions), the simplest methods of factor analysis with the least statistical assumptions perform best. As a result of this test it can generally be suggested to use principal factor analysis with an orthogonal rotation for such data. Selecting the number of factors to extract is difficult, however, the scree plot provides some useful help. For the test data, a low number of extracted factors gave the most informative results. Deleting or adding just 1 element in the input matrix can drastically change the results of factor analysis. Given that selection of elements is often rather based on availability of analytical packages (or detection limits) than on geochemical reasoning this is a disturbing result. Factor analysis revealed the most interesting data structures when a low number of variables were entered. A graphical presentation of the loadings and a simple, automated mapping technique allows extraction of the most interesting results of different factor analyses in one glance. Results presented here underline the importance of careful univariate data analysis prior to entering factor analysis. Outliers should be removed from the dataset and different populations present in the data should be treated separately. Factor analysis can be used to explore a large data set for hidden multivariate data structures.     

巨型矿床勘查新战略一一信息找矿

直接信息是最可靠的找矿信息,在矿产勘查中必须起先导的作用。直接信息与间接信息在一定条件和环境下可以互相转化的,只有在信息具有直接指示矿床存在和分布的特性时,它才会发挥实际的找矿效能。因此,多学科信息的收集和综合分析,是信息找矿战略实施的核心。信息找矿战略可以表述为“针对巨型矿床勘查,我们应当瞄准稳陷伏矿和难识别矿,以直接找矿信息（化探资料）为先导,综合地质和地球物理信息,迅速掌握全局,逐步缩小靶区     

Recognition of an enhanced multi-element geochemical signature of porphyry copper deposits for vectoring into mineralized zones and delimiting exploration targets in Jiroft area,SE Iran

Delimiting exploration targets using geochemical exploration data can be a challenging issue when different geochemical signatures represent the same deposit-type sought. In this regard, fuzzy operators have been used to integrate different geochemical evidence layers into a single model for generating target areas. In this paper, a GIS-based expected value function was adapted to integrate different geochemical evidence layers into a stronger geochemical signature for delimiting exploration targets. Then, the expected value function and fuzzy operators were compared. The comparison demonstrated that the former is more efficient than the later for generating a stronger geochemical evidence layer. The higher efficiency of the expected value function is because it simultaneously uses the value of all input variables and their relative importance in the process of integration. The proposed approach was evaluated by using a lithogeochemical data set for prospecting porphyry-Cu deposits in Jiroft area, Kerman province, southeast of Iran, as a case study.     

Joint Consistent Mapping of High-Dimensional Geochemical Surveys

Geochemical surveys often contain several tens of components, obtained from different horizons and with different analytical techniques. These are used either to obtain elemental concentration maps or to explore links between the variables. The first task involves interpolation, the second task principal component analysis (PCA) or a related technique. Interpolation of all geochemical variables (in wt% or ppm) should guarantee consistent results: At any location, all variables must be positive and sum up to 100 %. This is not ensured by any conventional geostatistical technique. Moreover, the maps should ideally preserve any link present in the data. PCA also presents some problems, derived from the spatial dependence between the observations, and the compositional nature of the data. Log-ratio geostatistical techniques offer a consistent solution to all these problems. Variation-variograms are introduced to capture the spatial dependence structure: These are direct variograms of all possible log ratios of two components. They can be modeled with a function analogous to the linear model of coregionalization (LMC), where for each spatial structure there is an associated variation matrix describing the links between the components. Eigenvalue decompositions of these matrices provide a PCA of that particular spatial scale. The whole data set can then be interpolated by cokriging. Factorial cokriging can also be used to map a certain spatial structure, eventually projected onto those principal components (PCs) of that structure with relevant contribution to the spatial variability. If only one PC is used for a certain structure, the maps obtained represent the spatial variability of a geochemical link between the variables. These procedures and their advantages are illustrated with the horizon C Kola data set, with 25 components and 605 samples covering most of the Kola peninsula (Finland, Norway, Russia).     

A scale-integrated exploration model for orogenic gold deposits based on a mineral system approach

Concept-based orogenic gold exploration requires a scale-integrated approach using a robust mineral system model.Most genetic hypotheses for orogenic gold deposits that involve near-surface or magmatic-hydrothermal fluids are now negated in terms of a global mineral system model.Plausible models involve metamorphic fluids,but the fluid source has been equivocal.Crustal metamorphic-fluid models are most widely-accepted but there are serious problems for Archean deposits,and numerous Chinese provinces,including Jiaodong,where the only feasible fluid source is sub-crustal.If all orogenic gold deposits define a coherent mineral system,there are only two realistic sources of fluid and gold,based on their syn-mineralization geodynamic settings.These are from devolatilization of a subducted oceanic slab with its overlying gold-bearing sulfide-rich sedimentary package,or release from mantle lithosphere that was metasomatized and fertilized during a subduction event,particularly adjacent to craton margins.In this model,CO_2 is generated during decarbonation and S and ore-related elements released from transformation of pyrite to pyrrhotite at about 500 ℃.This orogenic gold mineral system can be applied to conceptual exploration by first identifying the required settings at geodynamic to deposit scales.Within these settings,it is then possible to define the critical gold mineralization processes in the system:fertility,architecture,and preservation.The geological parameters that define these processes,and the geological,geophysical and geochemical proxies and responses for these critical parameters can then be identified.At the geodynamic to province scales,critical processes include a tectonic thermal engine and deep,effective,fluid plumbing system driven by seismic swarms up lithosphere-scale faults in an oblique-slip regime during uplift late in the orogenic cycle of a convergent margin.At the district to deposit scale,the important processes are fluid focussing into regions of complex structural geometry adjacent to crustal-scale plumbing systems,with gold deposition in trap sites involving complex conjugations of competent and/or reactive rock sequences and structural or lithological fluid caps.Critical indirect responses to defined parameters change from those generated by geophysics to those generated by geochemistry with reduction in scale of the mineral system-driven conceptual exploration.     

Controlling factors of groundwater hydrochemistry in a small island’s aquifer

Factor analysis was applied to the hydrochemical data set of Manukan Island in order to extract the principal factors corresponding to the different sources of variation in the hydrochemistry. The application of varimax rotation was to ensure the clear definition of the main sources of variation in the hydrochemistry. The geochemical data of dissolved major, minor and trace constituents in the groundwater samples indicates the main processes responsible for the geochemistry evolution. By using Kaiser normalization, principal factors were extracted from the data for each location. The analysis reveals that there are four sources of solutes: (1) seawater intrusion; (2) leaching process of underlying rock mediated by pH; (3) minerals weathering process and (4) dissolution of carbonate minerals characterized by high loadings of Ca, Zn and Mg. Such processes are dominated by the significant role of anthropogenic impact from the over abstraction of fresh water from the aquifer. Those factors contributed to the changes of the groundwater geochemistry behavior explain the effect of rising extraction of freshwater from the aquifer.     

安徽省兆吉口铅锌矿床成矿地球化学机制研究

自20世纪30年代起, 勘查地球化学就在矿产资源勘查领域发挥着重要作用。目前, 矿业界对勘查地球化学回归到基础勘查理论研究有着明确的需要。多维异常体系应用基础理论的提出是我国学者在该领域的积极响应。多维异常体系定义为“在特定的成矿地质时期, 成矿系统中存在的空间有序共存、成因机理各异、成矿指示递进的多属性异常体系”。其中, 多属性异常的形成机制及其在成矿空间中的结构关系, 是探讨矿床成矿地球化学机制和指导矿产勘查的基础, 同时也是勘查地球化学研究的前沿方向。本论文以位于安徽省东至县的兆吉口浅成低温热液型铅锌矿床为研究对象, 通过元素质量迁移定量计算, 研究典型剖面上元素活动规律, 构建矿致异常结构模式, 揭示矿床成矿地球化学机制; 利用图示方法展现不同水平断面上元素异常分布形态, 为深部成矿预测指明方向; 利用分形模型和基于成分数据理论的主成分分析、因子分析等方法, 研究地表岩屑中元素分布特征及影响因素, 指导研究区外围矿床地球化学勘查。     

Using fuzzy logic in a Geographic Information System environment to enhance conceptually based prospectivity analysis of Mississippi Valley‐type mineralisation

Geographic Information Systems (GIS) provide an efficient vehicle for the generation of mineral prospectivity maps, which are products of the integration of large geological, geophysical and geochemical datasets that typify modern global‐scale mineral exploration. Conventionally, two contrasting approaches have been adopted, an empirical approach where there are numerous deposits of the type being sought in the analysed mature terrain, or a conceptual approach where there are insufficient known deposits for a statistically valid analysis. There are also a variety of potential methodologies for treatment of the data and their integration into a final prospectivity map. The Lennard Shelf represents the major Mississippi Valley‐type (MVT) province in Australia; however, there are only 13 deposits or major prospects known, making an empirical approach to prospectivity mapping impractical. Instead, a conceptual approach was adopted, where critical features that control the location of MVT deposits on the Lennard Shelf, as defined by widely accepted genetic models, were translated into features related to fluid pathways, depositional traps and fluid outflow zones, which can be mapped in a GIS and categorised as either regional or restricted diagnostic, or permissive criteria. All criteria were derived either directly from geological and structural data, or indirectly from geophysical and geochemical datasets. A fuzzy‐logic approach was adopted for the prospectivity analysis, where each interpreted critical feature of the conceptual model was assigned a weighting between 0 and 1 based on its inferred relative importance and reliability. The fuzzy‐logic method is able to cope with incomplete data, a common problem in regional‐scale exploration datasets. The data were best combined using the gamma operator to produce a fuzzy‐logic map for the prospectivity of MVT deposits on the southeastern Lennard Shelf. Five categories of prospectivity were defined. Importantly, from an exploration viewpoint, the two lowest prospectivity categories occupy ～90% and the highest two categories only 1.6% of the analysed area, yet eight of the 13 known MVT deposits lie in the latter and none in the former: i.e. all lie within ～10% of the area, despite the fact that deposit locations were not used directly in the analysis. The propectivity map also defines potentially mineralised areas in the central southeastern Lennard Shelf and the southern part of the Oscar Ranges, where there are currently no known deposits. Overall, the analysis demonstrates the power of fuzzy‐logic prospectivity mapping on a semi‐regional to regional scale, and emphasises the value of geological data, particularly accurate geological maps, in exploration for hydrothermal mineral deposits that formed late in the evolution of the terrain under exploration.     

基于RCGA的PPC模型在化探异常识别与提取中的应用

为了进行地球化学异常的识别与提取,针对化探数据的特点,本文提出了一种将高维降维技术——投影寻踪分类(PPC)模型与实数编码遗传算法(RCGA)相结合的计算方法,分析了运用RCGA-PPC模型进行化探异常识别与提取的关键技术问题,并在MATLAB环境下开发了该方法的软件应用模块。以云南个旧地区水系沉积物地球化学数据为例,选取区域内Sn、Cu、Pb、Zn、As、Cd等主要成矿元素及与成矿关系密切的9种元素作为计算变量,利用RCGA-PPC模型对其进行处理和异常识别。研究表明:RCGAPPC模型中最佳投影值较高的地区与该区域实际矿床(点)吻合情况较好。该模型对化探异常的识别能力较强,是一种有效的化探多元素综合异常识别与提取方法。