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.     

A comparison study of the C–A and S–A models with singularity analysis to identify geochemical anomalies in covered areas

Fractal/multifractal modeling of geochemical data is an interesting topic in the field of applied geochemistry. Identification of weak anomalies for mineral exploration in covered areas is one of the most challenging tasks for utilization of geochemical data. In this study, three fractal models, consisting of the concentration–area (C–A), spectrum–area (S–A) and singularity index models were applied to identify geochemical anomalies in the covered area located in the Chaobuleng Fe polymetallic district, Inner Mongolia (China). The results show that (1) the grassland cover weakens the concentrations of geochemical elements; (2) the C–A model has a limitation to identify weak anomalies in covered areas; (3) the S–A model is a powerful tool to decompose mixed geochemical patterns into a geochemical anomaly map and a varied geochemical background map but suffers edge effects in an irregular shaped study area; and (4) the singularity index is a useful tool to identify weak geochemical anomalies.     

Comparison of U-spatial statistics and C–A fractal models for delineating anomaly patterns of porphyry-type Cu geochemical signatures in the Varzaghan district,NW Iran

The delineation of populations of stream sediment geochemical data is a crucial task in regional exploration surveys. In this contribution, uni-element stream sediment geochemical data of Cu, Au, Mo, and Bi have been subjected to two reliable anomaly-background separation methods, namely, the concentration-area (C–A) fractal and the U-spatial statistics methods to separate geochemical anomalies related to porphyry-type Cu mineralization in northwest Iran. The quantitative comparison of the delineated geochemical populations using the modified success-rate curves revealed the superiority of the U-spatial statistics method over the fractal model. Moreover, geochemical maps of investigated elements revealed strongly positive correlations between strong anomalies and Oligocene–Miocene intrusions in the study area. Therefore, follow-up exploration programs should focus on these areas.     

Application of fractal content-gradient method for delineating geochemical anomalies associated with copper occurrences in the Yangla ore field,China

Fractal and multi-fractal content area method finds application in a wide variety of geological, geochemical and geophysical fields. In this study, the fractal content-gradient method was used on 1:10,000 scale to delineate geochemical anomalies associated with copper mineralization. Analysis of geochemical data from the Yangla super large Cu-Pb-Zn polymetallic ore district using the fractal content-gradient method, combined with other geological data from this area, indicates that ore-prospecting in the ore district should focus on Cu as the main metal and Pb-Zn and Au as the auxiliary metals. The types of deposits include (in chronological order) re-formed sedimentary exhalative (SEDEX), skarns, porphyries, and hydrothermal vein-type deposits. Three ore-prospecting targets are divided on a S–N basis: (1) the Qulong exploration area, in which the targets are porphyry-type Cu deposits; (2) the Zongya exploration area, where the targets are porphyry-type Cu and hydrothermal vein-type Cu-Pb polymetallic deposits; and (3) the Zarelongma exploration area, characterized mainly skarn-type “Yangla-style” massive sulfide Cu-Pb deposits. Our study demonstrates that the fractal content-gradient method is convenient, simple, rapid, and direct for delineating geochemical anomalies and for outlining potential exploration targets.     

Application of geostatistical hierarchical clustering for geochemical population identification in Bondar Hanza copper porphyry deposit

Several machine learning approaches have been developed for the identification of geochemical populations. In these approaches, the geochemical elements are usually the sole quantitative variables used as inputs for geochemical population recognition. This means that the presence of other qualitative variables, such as geological information, is overlooked in the analysis. Hierarchical clustering, as an unsupervised machine learning method, is a common approach for dimensional reduction in the analysis of geochemical data. In this study, an alternative to this technique, known as geostatistical hierarchical clustering (GHC), is applied to identify geochemical populations in 3D in the Bondar Hanza copper porphyry deposit, Iran. In this paradigm, the qualitative geological variables can also be incorporated for geochemical population identification, in addition to qualitative geochemical elements. In this study, an innovative solution is presented to tune the weighting parameters of each variable in GHC, based on the associations that the clusters (i.e., geochemical populations) should have with the geological information. The results are compared with k-means and number–size fractal/multifractal (N–S) methods. As a result, GHC showed better agreement with alterations, rock types, and mineralization zones in this deposit. Finally, some important instructions are provided for further mineral exploration.     

Separation of a geochemical anomaly from background by fractal and U-statistic methods,a case study: Khooni district,Central Iran

Separation of geochemical anomalies from background are one of the important steps in mineral exploration. The Khooni mineral district (Central Iran) has complex geochemical surface expression due to a complex geological background. This region was chosen as a study area for recognition of the spatial distribution of geochemical elements and separating anomalies from background using stream sediment geochemical data. In the past decades, geochemical anomalies have been identified by means of various methods. Some of these separation methods include: statistical analysis methods, spatial statistical methods and fractal and multi-fractal methods. In this article, two efficient methods, i.e. U-statistics and the fractal concentration-area for separation and detection of anomalous areas of the background were used. The U spatial statistic method is a weighted mean, which considers sampling point positions and their spatial relation in the estimation of anomaly location. Also, fractal and multi-fractal models have also been applied to separate anomalies from background values. In this paper, the concentration–area model (C–A) was suggested to separate the anomaly of background. For this purpose, about 256 stream sediment samples were collected and analyzed. Then anomaly maps of elements were generated based on U spatial statistics and the C-A fractal methods for Au, As and Sb elements. According to obtained results, the U-statistics method performed better than C-A method. Because the comparisons of the known deposits and occurrences against the anomalous area created using thresholds from U-statistics and C-A method show that the spatial U-statistics method hits all of 3 known deposits and occurrences, the C-A fractal method hits 1 and fails 2. In addition, the results showed that these methods with regard to spatial distribution and variability within neighboring samples, in addition to concentration value frequency distributions and correlation coefficients, have more accurate results than the traditional approaches.     

A comparative study of two modes for mapping felsic intrusions using geoinformatics

Identifying felsic intrusions is an essential task in support of mineral exploration because the intrusions can be a source of energy and metals for magmatic-hydrothermal mineralization. In this paper, two models for mapping felsic intrusions are compared based on regional geochemical and geophysical data. Geochemical data as a type of compositional data which carry relative information should be preprocessed using log-ratio transformation. The first model, a factor ratio (F2/F1) model, was developed based on the chemical characteristics of the felsic intrusions, which are rich in K₂O and high field strength elements (F2), but poor in Fe₂O₃ and compatible elements (F1). The second model, a hybrid model that combines principal component analysis and local singularity analysis, was based on the chemical and physical properties of the felsic intrusions. The results showed that (1) raw geochemical data should be processed using log-ratio transformation prior to multivariate data analysis to avoid spurious correlations between variables, and (2) the hybrid model performed better than the ratio of factors model for inferring felsic intrusions in the study area. The felsic intrusions mapped in this study provide information that can support further mineral exploration in the Dong Ujimqin Fe–Cu polymetallic district, Inner Mongolia, northern China.     

Application of the content–area (C–A) fractal model and prediction–area (P–A) plot for mineral prospectivity modeling in the Luchun area of Yunnan Province,China

This method of assigning weights based on expert opinion introduces bias when we are evaluating the relative importance of evidence values. In this paper, we used a prediction–area (P–A) plot method and content–area (C–A) fractal model to estimate the weight of each evidence map. In this paper, we used the content region (C–A) fractal model to divide the evidence maps to the threshold of the corresponding dimensions. The P–A plot approach is an objective data-driven approach for evaluating map weights. Using geochemical layer and remote sensing, hydroxyl layers as weight evidence maps are the highlights of this study. We use the P–A method from which we can evaluate the predictive ability of each evidence map with respect to the known ore occurrences. We used the P–A plot for weighting each evidence map and choosing the appropriate threshold for predictor maps in the Luchun area of Yunnan Province, China. The method adopted in this paper can improve the prediction efficiency of ore prospecting.     

Introducing a new approach of geochemical anomaly intensity index (GAII) for increasing the probability of exploration of shear zone gold mineralization

Increasing the prediction rate in the identification of mineralization zones using the stream sediment geochemical data is an essential issue in the regional exploration stage. The various univariate (such as fractal and probability plot (PP) methods) and multivariate methods (such as principal component analysis (PCA)) have been performed for interpreting the geochemical data and detecting the mineralization areas. In this study, a new geochemical criterion named geochemical anomaly intensity index (GAII) was proposed for geochemical anomaly mapping. This approach was developed based on the PCA method and the catchment basin coefficient (CBC). The GAII as a weighted geochemical index is calculated using the mineralization principal component (MPC) scores and CBC. GAII can be mapped and utilized for geochemical anomaly mapping and detecting the mineralization areas. Besides, GAII can identify paragenesis elements better than the current methods. In this research, GAII was successfully used to generate geochemical anomaly maps on shear zone gold mineralization in the southwest of Saqqez, NW Iran. The geochemical data have been divided into three groups based on catchment basins and the host rock type. Then the MPCs and paragenesis elements of Au mineralization have been obtained individually using PCA. Three mineralization paragenesis groups consisting of (Au, Sn), (Au, W), and (Au, As, Sb and Ba) have been recognized for different catchment basins of the southwest of Saqqez district using PCA. GAII was calculated and mapped based on the CBC_{(Au, Sn)}, CBC_{(Au, W)}, CBC_{(Au, As, Sb, Ba)}, and their MPC scores. GAII accurately detected the Au mineralization zones and improved the geochemical anomaly map in this area compared to the PP method, concentration-area fractal model, and U-spatial statistics method. The results demonstrated that GAII was successfully used for (a) identifying the mineralization paragenesis elements, (b) intensifying the geochemical anomaly, and (c) increasing the prediction rate of mineralization zones. The shear zone gold mineralization areas in the southwest of Saqqez district were effectively detected using this new data analysis approach. GAII has provided better results than the current PP method, concentration-area fractal model, and U-spatial statistics method.     

Fractal-Based Wavelet Filter for Separating Geophysical or Geochemical Anomalies from Background

The widely used wavelet filtering technique holds potential to approach anomaly–background separation in geophysical and geochemical data processing. Wavelet statistics provide crucial information on such filtering methods. In general, conventional (Gaussian-type) statistical modeling is insufficient to adequately describe the heavily tailed and sharply peaked (at zero) distribution of the wavelet coefficients of irregular geo-anomaly patterns. This paper demonstrates that the cumulative (frequency) number of the wavelet coefficient yields a power-law scaling relationship with the coefficient based on wavelet transform of a fractal/singular measure. This wavelet coefficient–cumulative number power-law model is proven to be more flexible and appropriate than the Gaussian model for characterizing the scaling nature of the coefficient distribution. Accordingly, a fractal-based filtering technique is developed based on the wavelet statistical model to decompose mixed patterns into components based on the distinct self-similarities identified in the wavelet domain. The decomposition scheme of the fractal-based wavelet filtering method considers not only the coefficient frequency distribution but also the fractal spectrum of singularities and the self-similarity of real-world features. Finally, a synthetic data test and real applications from two metallogenic provinces of China are used to validate the proposed fractal filtering method for anomaly–background separation and identification of geophysical or geochemical anomalies related to mineralization and other geological features.     

The advantages of using n-alkanes, triterpane, and steranes to determine the characterization of sedimentary organic matter

The current geochemical study of n-alkanes, steranes, and triterpanes in bitumen from the Late Maastrichtian–Paleocene El Haria organic-rich facies in West of Gafsa, southern Tunisia, was performed in order to characterize with accuracy their geochemical pattern. The type of organic matter as deduced from n-alkanes, steranes, and triterpanes distributions is type II/III mixed oil/gas prone organic matter. Isoprenoids and biomarkers maturity parameters (i.e., T _s/T _m, 22S/(22S?+?22R) of the C₃₁ αβ-hopanes ratios, 20S/(20R?+?20S) and ββ/(ββ?+?αα) of C₂₉ steranes), revel that the organic-rich facies were deposited during enhanced anoxic conditions in southern Tunisa. The organic matter is placed prior to the peak stage of the conventional oil window (end of diagenesis–beginning of catagenesis). All these result are suggested by total organic carbon analysis, bitumen extraction and liquid chromatography data. Thus, the n-alkanes, triterpane, and steranes study remains valuable and practical for geochemical characterization of sedimentary organic matter.     

Comparison of mineralization pattern of geochemical data in spatial and position-scale domain using new DWT- PCA approach

In the current research to determine the mineralization pattern and discuss the mineralization components, the information of position - scale domain of geochemical data has been analyzed. A new method is proposed based on coupling discrete wavelet transforms (DWT) and principal component analysis (PCA) for mineralization elements forecasting applications. The results of this study indicate the potential of DWT–PCA method for geochemical data processing. Wavelet transform (WT), as a multi-spectral analysis method, can decompose the spatial and temporal signals into different frequencies. The features of mineralization can be identified using the position - scale domain of geochemical data that may not be achievable in spatial domain. The geochemical data from the Dalli region have been processed in the spatial domain using PCA. The surface geochemical data of 30 elements have been transformed to position–scale domain using two-dimensional discrete wavelet transform (2DDWT). Wavelet functions (WFs) of Haar, Coiflet2, Biorthogonal3.3 and Symlet7 have been applied separately to decompose the geochemical data to high and low frequencies in one level. To obtain more accurate and complete information of mineralization, a new index has been presented based on wavelet coefficients. Based on this new index, significant results have been obtained by using PCA of the index. The coefficients distribution map (CDM) as a new exploratory criterion has been generated based on 2DDWT to show the geochemical distribution map (GDM). Finally, the results of WT have been compared with the results of spatial domain and the best method of wavelet for interpretation of geochemical data has been introduced. The results of geochemical data analysis by DWT–PCA approach have been confirmed by the exploratory drillings in the study area.     

多维分形理论和地球化学元素分布规律

多维分形模型不仅采用常规的低阶矩统计, 而且采用高阶矩统计对多维分形分布进行度量, 从而能较细致地刻划正常值以及异常值.地球化学元素的正常值往往服从统计学中的大数定量, 即满足正态分布或对数正态分布, 然而异常值会服从分形分布(Preato).介绍了多维分形领域中的最新发展以及在地球化学研究中特别是研究超常元素空间分布和富集规律中的应用.结果表明, 通常的统计方法只对应于多维分形围绕均值周围的局部特征.为了有效地研究异常值的分布和富集规律, 建议采用高阶矩统计方法和多维分形方法, 并给出了两种分析地球化学元素, 并突出异常值贡献的方法.这些方法不仅可应用于研究微量元素的空间分布和富集规律, 而且可以区分地球化学背景与矿化有关的异常值.还介绍了该方法在对加拿大B.C.省西北部Mitchell-Sulphurets地区金铜矿化蚀变带研究中的应用.      

Geochemical anomaly definition using multifractal modeling,validated by geological field observations: Siah Jangal area,SE Iran

Recognition of geochemical anomalies is a pivotal assignment in exploration projects. This study aims to delineate different AuCu geochemical anomalies using number-size (N-S) and concentration-area (C-A) multifractal models in the Siah Jangal area, SE Iran. In this research, lithogeochemical datasets were applied for the exploration of Au and Cu. A comparison between geochemical anomaly maps based on the N-S and C-A fractal models shows the N-S fractal modeling is a powerful tool for separation of weak elemental geochemical anomalies in all of sampling zones. Based on a comparison between the results of these two methods and field studies, the geochemical anomaly zones, defined by the N-S fractal model, are more accurate than those recognized by the C-A fractal model. The obtained results of the N-S and C-A fractal models have been interpreted with the extensive set of information including structural interpretation, geological and alteration data. Au and Cu mineralization in the Siah Jangal area are hosted mainly by Oligocene-Miocene sub-volcanic rocks, especially strongly altered porphyric quartz diorite, hornblende diorite and diorite. Moreover, the positive dependence between various alteration zones and high concentrations of Au and Cu proves that strongly anomalous areas are correlated with these alteration zones. High grade Au (> 1000 ppb) and Cu (>150 ppm) are associated with the altered sub-volcanic rocks in the northern, eastern, and SW parts of the study area. Therewith, the strong anomaly populations are mostly occurred within the fault and fracture systems in the study area. This is a promising signal because quartz-sulfide veins and veinlets are associated with such structures.     

2011~2020年勘查地球化学数据处理研究进展

我国建立了包含海量数据的高质量的勘查地球化学数据库,为矿产勘查、环境评价和地质调查等提供了重要的数据支撑。如何高效处理勘查地球化学数据,并从中发掘和识别深层次信息一直是勘查地球化学学科研究的热点和前沿领域。本文在系统调研国内外学者过去十年发表的论著基础上,对勘查地球化学数据处理方法进行分析与对比,从勘查地球化学数据库建设、地球化学异常识别及其不确定性评价等方面概述了我国近十年来在该领域取得的主要研究进展,包括:(1)分形与多重分形模型由于考虑了地球化学空间模式的复杂性和尺度不变性,在全球范围内得到极大的发展和推广,我国学者引领了基于分形与多重分形的勘查地球化学数据处理;(2)机器学习和大数据思维开始在该领域启蒙,并迅速得到关注,正在成为研究热点和前沿领域,我国学者率先开展基于机器学习算法的勘查地球化学大数据挖掘研究;(3)我国学者需要进一步加强勘查地球化学数据缺失值处理以及成分数据闭合效应研究。今后该领域应进一步加强对弱缓地球化学异常识别、异常不确定性评价以及异常识别与其形成机理相结合等方面的研究。     

利用分形统计学提取化探数据中的 隐蔽信息并圈定地球化学异常

阐述了分数布朗运动与地质统计学的关系及用变差函数求分数布朗运动分维数的方法,提出用改进的方位－分维估值法预测化探数据的区域背景和局部背景,从而圈定出地球化学异常．最后给出了山东平邑铜石金矿田１∶５万水系沉积物地球化学数据的实际应用效果．     

分形方法在微量元素油气化探异常下限分析中的应用

在研究松辽盆地（松嫩平原南部）土壤样品中与油气化探相关的I、S、Cl、Sr四种微量元素地球化学场数据特征的基础上,应用传统方法和分形方法分别确定其地球化学异常下限,并对研究区土壤元素地球化学异常区域进行了固定。通过对比,发现分形方法圈定的异常区域与已知油气区吻合更好,该方法既避免了遗漏有用的油气信息,同时也剔除了部分干扰因素。     

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.     

Exploratory Data Analysis Applied in Mapping Multi-element Soil Geochemical Anomalies for Drill Target Definition: A Case Study from the Unpha Layered Non-magmatic Hydrothermal Pb-Zn Deposit,DPR Korea

A factor analysis was applied to soil geochemical data to define anomalies related to buried Pb-Zn mineralization. A favorable main factor with a strong association of the elements Zn, Cu and Pb, related to mineralization, was selected for interpretation. The median + 2MAD (median absolute deviation) method of exploratory data analysis (EDA) and C-A (concentration-area) fractal modeling were then applied to the Mahalanobis distance, as defined by Zn, Cu and Pb from the factor analysis to set the thresholds for defining multi-element anomalies. As a result, the median + 2MAD method more successfully identified the Pb-Zn mineralization than the C-A fractal model. The soil anomaly identified by the median + 2MAD method on the Mahalanobis distances defined by three principal elements (Zn, Cu and Pb) rather than thirteen elements (Co, Zn, Cu, V, Mo, Ni, Cr, Mn, Pb, Ba, Sr, Zr and Ti) was the more favorable reflection of the ore body. The identified soil geochemical anomalies were compared with the in situ economic Pb-Zn ore bodies for validation. The results showed that the median + 2MAD approach is capable of mapping both strong and weak geochemical anomalies related to buried Pb-Zn mineralization, which is therefore useful at the reconnaissance drilling stage.