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.     

S-A多重分形法在地球化学异常 圈定中的对比应用—以甘肃文康地区为例

通过对甘肃省陇南文康地区的地质背景分析,采用区域1:5万水系沉积物测量的17种常量元素含量数据,分别运用传统统计学方法、S-A（能谱密度-累计面积）多重分形滤波法,确定成矿元素（以Au为例）异常下限值,圈定化探异常。结果表明,与传统方法相比,S-A多重分形法所圈定的异常面积小、数量多,传统统计法圈定的面积较大;两种方法圈定的异常展布方向基本一致,与研究区内已知的矿点吻合度高。本次研究认为S-A多重分形法所圈定的异常基本位于传统方法所圈定异常的内带之中,重点突出,与矿床空间位置对应关系较好,便于野外开展工作。同时在传统异常周边提取了部分低背景区的弱异常,这些弱异常可作为下一步找矿工作的新靶区。     

内蒙古白音郭勒地区地球化学异常提取方法对比研究

勘查地球化学的基础性问题是如何合理地提取地球化学异常下限,这是影响勘查地球化学在矿产勘查中效果的主要因素。利用SPSS软件,通过含量对数频率直方图,结合偏度、峰度检验法检验元素的含量分布型式,发现研究区的Cu、Ag、Pb、Zn元素不服从正态或对数正态分布,而呈现多峰态、高丛集的多重分形特征。采用传统的统计学方法、85%累计频率法、含量-总量多重分形法提取相关元素地球化学异常,并进行对比研究。研究结果表明,基于元素空间分布及含量分布规律的含量-总量多重分形方法,有效克服了传统方法的局限,比较客观地反映研究区的地球化学异常信息,是研究地球化学异常比较合理的方法。     

Expert System for Statistical Prediction of Mineral Deposits on Middle and Large Scales

The expert system for statistical prediction of mineral deposits on middle and large scales takes the system of scientific exploration theories, criteria and methods proposed by Professor Zhao Pengda as the field expert knowledge. At present the developed system focuses on two aspects: synthetic exploration and quantitative exploration. Among the three basic theories for the prediction of deposits, it highlights the applications of seeking anomaly theory. This system is characteristic in the determination of geological background, the study of geological anomalies and the delineation of geological background, the study of geological anomalies and the delineation of mineralization anomalies. The system combines closely the knowledge base, method base and database ,integrates the input and output information of multi-sources and multi-variables, data , graphs and imagine processing system and inquiring system as a whole . So the system can meet in general all kinds of demands in statistical prediction of m     

Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data,a case study from Gangdese,Tibet, western China

Identifying geochemical anomalies from background is a fundamental task in exploration geochemistry. The Gangdese mineral district in western China has complex geochemical surface expression due to complex geological background and 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. The results illustrate that weak anomalies are hidden within the strong variance of background and are not well identified by means of inverse distance weighted; neither are they clearly identified by the C–A method if this method is applied to the whole study area. On the other hand, singularity values provide new information that complements use of original concentration values and can quantify the properties of enrichment and depletion caused by mineralization. In general, producing maps of singularities can help to identify relatively weak metal concentration anomalies in complex geological regions. Application of singularity mapping technique in Gangdese district shows local anomalies of Cu are not only directly associated with known deposits in the central part of the study area, but also with E–W and N–E oriented faults in the north of the study area. Both types of anomalies should be further investigated for undiscovered Cu mineral deposits.     

地球化学元素空间定量组合求异模型及其应用

由于成矿过程的多期性与多源性,成矿区地球化学元素在空间上表现出共生组合特征。为刻画组合特征的类型与特点,提出一种地球化学元素空间定量组合求异方法,构建了因子泛克里格模型、多元序列典型趋势面模型及特征分析组合模型。将上述3种空间定量组合求异模型应用于云南哀牢山墨江地区1∶20万化探数据进行处理分析,目的在于从多元角度刻画元素异常,提高矿异常预测精度。计算结果表明:该区存在4种异常类型,反映了不同地质条件下元素共生组合及其在成岩成矿过程中的内在组配机制和空间关系。由此得出结论:在成矿预测过程中应按不同的定量组合异常类型及其相应地质背景进行预测。     

遥感异常与地球化学指标间的映射关系模型探索

采用改进的遥感异常提取方法,以甘肃金川地区的LandSat 8遥感影像数据为基础,结合主成份分析方法及低通滤波后期处理,得到空间规律性较强的烃基及铁染异常;进而对测区的土壤地球化学取样分析数据,利用统计学方法寻找不同元素异常(或地球化学指标)的内在关联,并建立土壤地球化学指标比值与遥感异常之间的关系模型.继续构建出基于...     

Spatial filtering of exploration geochemical data using EDA and robust statistics

The spatial filtering techniques that are used for the analysis and interpretation of exploration geochemical data to define regional distribution patterns or to outline anomalous areas are, in most cases, based on non-robust statistical methods. The performance of these techniques is heavily influenced by the presence of outliers that commonly exist in the data. This study describes a number of filtering techniques motivated by the development of exploratory data analysis (EDA) and robust statistical procedures. These are the median filter (MF) and the adaptive trimmed mean filter (ATM) for the smoothing of regional geochemical data to reduce spurious variations; two new filters, the fence filter (FF) and the notch filter (NF), have been developed to define geochemical anomalies.The application of the spatial filtering techniques is illustrated by Zn data from approximately 3100 stream sediment samples taken in a regional geochemical survey over 25,000 km² of the western margin of the São Francisco Basin, Brazil. Regional distribution patterns for Zn obtained by the MF and ATM filters are clearly related to known stratigraphic units. Anomaly filtering using the FF and NF has delineated most known base metal and gold occurrences, as well as a number of anomalies located in geologically favourable environments but unrelated to any known mineralization. The two anomaly filters have, for the most part, defined the same anomalies in the study area but only the NF highlights the anomaly associated with the important Morro Agudo Pb-Zn deposit, which is too subtle to be immediately apparent in the unprocessed data.     

测定水浸土壤样的水中氦找铀矿方法的研究

本文讨论了利用离子泵测氦仪,测定水浸土壤样的水中氦的找铀矿方法。通过十项浸泡条件及测量试验,确定了处理土壤样的水浸技术。在矿床上取样分析的结果表明,在陡倾角地层的条件下,壤中氦异常出现在深部铀矿化范围的水平投影位置上,说明该方法对寻找隐伏铀矿是有效的,可用于地表详测等找矿工作。     

中国钴地球化学异常特征、成因及找矿远景区预测

近年来钴资源的战略地位急剧提升,钴资源勘查也日益受到重视.但钴的成矿作用特殊,极低密度地球化学填图能否为钴资源勘查提供可靠依据有待研究.对全国基准计划获得的深层汇水域沉积物钴地化异常开展研究,建立矿床与异常空间位置指数,定量评价矿床与异常相对位置关系.发现钴异常的形成主要受（超）基性岩控制,少数与矿化作用有关,个别钴异常与泥质岩和次生富集等因素有关.中国与欧洲（FOREGS计划）结果均表明,风化型、热液型、岩浆型钴矿床与钴异常空间对应关系依次减弱.在此基础上,结合中国钴成矿地质背景,在华南钴成矿带、华北克拉通北缘东段等圈定了若干钴成矿远景区.     

A review of geochemical background concepts and an example using data from Poland

One of the most crucial issues of recent environmental sciences is the topic of background concentrations of elements and organic compounds in various abiotic and biotic systems. The relationship between natural and anthropogenically altered concentrations of chemical species is a question that involves many implications in the geosciences, environmental and biological sciences, toxicology, and other related disciplines. This is especially important when interpreting geochemical and biogeochemical anomalies of toxic elements and/or organic compounds in various media. To better understand the potential impact of hazardous substances in the environment, we must become more familiar with their spatial and temporal distribution and with their behavior under different physico-chemical and biotic conditions. This review presents an assessment of the geochemical background concept as used by various authors. Different assumptions and approaches to this topic are presented, including direct, statistical, and integrated methods. Based on the results derived from geochemical and biogeochemical studies performed in selected forest ecosystems of Poland, an integrated method is presented. As a consequence of data processing, a normal distribution of data points was obtained using an iterative 2σ-technique. This method of estimating geochemical background is feasible and can be used for setting environmental quality standards or for studying the impact of anthropogenic pollution sources on the environment.     

Exploratory and spatial data analysis (EDA–SDA) for determining regional background levels and anomalies of potentially toxic elements in soils from Catorce–Matehuala, Mexico

The threshold between geochemical background and anomalies can be influenced by the methodology selected for its estimation. Environmental evaluations, particularly those conducted in mineralized areas, must consider this when trying to determinate the natural geochemical status of a study area, quantifying human impacts, or establishing soil restoration values for contaminated sites. Some methods in environmental geochemistry incorporate the premise that anomalies (natural or anthropogenic) and background data are characterized by their own probabilistic distributions. One of these methods uses exploratory data analysis (EDA) on regional geochemical data sets coupled with a geographic information system (GIS) to spatially understand the processes that influence the geochemical landscape in a technique that can be called a spatial data analysis (SDA). This EDA–SDA methodology was used to establish the regional background range from the area of Catorce–Matehuala in north-central Mexico. Probability plots of the data, particularly for those areas affected by human activities, show that the regional geochemical background population is composed of smaller subpopulations associated with factors such as soil type and parent material. This paper demonstrates that the EDA–SDA method offers more certainty in defining thresholds between geochemical background and anomaly than a numeric technique, making it a useful tool for regional geochemical landscape analysis and environmental geochemistry studies.     

Geochemical prospecting in complex sample media-multivariate data analysis of indirect observations (PLS-regression between modal mineralogy and geochemistry)

Geochemical exploration in secondary environments can be viewed as a particular manifestation of indirect geological observation. Geochemical anomalies in complex sample media reflect dispersion signatures, generally much disguised by secondary or higher-order mechanical and physico-chemical processes such as mixing, comminution, dilution, (re)transportation, weathering etc. Such complexities often make a thorough understanding of the origin of any particular sample type difficult ot obtain. The objective of data analysis in this context is to convert the geochemical data into a meaningful “signal”, particularly useful for prospecting, and other, in this case irrelevant, variability or “noise”. The experience of the last decades of practical exploration has clearly shown that statistical as well as geographical geochemical anomaly patterns are multi-element signatures. Using suitable multivariate statistical procedures (in the present case principal components modelling), it is possible to simultaneously define both a background data model and to quantify multivariate geochemical anomalies. This type of data analysis is guided very strongly by geological interaction, in which the emphasis is on modelling the background population(s), coupled with geographic plotting facilities. This outlier-screening facility is critical for many types of geochemical data evaluation. An example of this approach is described below. Another application of indirect multivariate data analysis is represented by PLS (Partial Least Squares) regression, which is a supervised pattern recognition and regression technique. We use it here to predict modal scheelite occurrences from regional stream-sediment data.     

Mapping geochemical singularity using multifractal analysis: Application to anomaly definition on stream sediments data from Funin Sheet,Yunnan, China

Most conventional statistical methods aiming at defining geochemical concentration thresholds for separating anomalies from background have limited effectiveness in areas with complex geological settings. This paper uses multifractal analysis to combine the characteristics of geochemical frequency distribution and spatial dispersion in order to map geochemical singularities instead of using conventional statistically derived concentration thresholds. The model, termed radius–areal Productivity (r–P) model, employs a stable measure and a scale-increasing method to estimate geochemical singularities spatially on geochemical landscape for delineating potential anomalies. The model is applied to geochemical data of regional stream sediments from the Funin Sheet, Yunnan, China.     

一种研究区域化探资料的新方法及其应用

介绍了一种对区域化探资料进行二次开发研究的方法。这个方法综合运用了常规物化探数据处理的方法技术、经验公式等 ,对元素的原始含量数据进行处理 ,将地球化学信息与地质背景信息融为一体 ,形成一种新型的地球化学综合信息图示资料 ,可屏蔽三分之二强的非矿及大量岩性异常 ,使其与矿床和矿化相关的异常信息一目了然 ,达到快速、有效的提取和利用有用信息的目的     

利用快速聚类分析分区确定化探背景上限的方法

区域化探原生背景与异常形成于相互独立的地质过程。在岩性复杂区,不同地质单元内背景与异常的成因可能完全不同,划定统一的背景上限显然是不科学的,合理的解决方案是分区确定化探背景上限。这里提出一种采用主要造岩元素为指标,采用快速聚类分析方法划分地质单元并分别计算背景上限的简便方法。以1:200 000郴县幅化探数据为例进行试验,结果表明,该方法能有效地去除不同地质体类型背景对异常的影响,不仅能突出矿区异常,也能削弱高背景区非矿致异常,缩小靶区面积,识别微弱异常。     

油气化探数据的背景分析方法研究

油气化探的异常确定一直是争论较多的问题之一，尤其是在存有背景差异的情况下就更为突出。分别划分异常下限带有较强的人为因素，而趋势分析面对情况比较复杂的地学空间场，也会因为拟合度或边界效应等原因而出现较大的误差。作者在本文中采用了傅立叶带通滤波，消除了空间场中的北景差异，在塔北油气勘探的应用中取得了较好的效果。     

分形奇异(特征)值分解方法与地球物理和地球化学异常重建

地球物理和地球化学异常是找矿的重要依据.地球物理和地球化学异常取决于地层、构造在成矿时间上的多样性与空间上排列、叠置的复杂性.地层、构造因素是构造、岩浆、沉积与成矿地球化学等多种动力学过程的综合反映.这些岩石和构造的因素以及动力过程相互渗透和影响决定了最终地质、地球物理与地球化学场.本文提出的在GIS环境下实现的分形奇异值分解(MSVD) 异常重建方法, 不仅可以提取地球物理和地球化学等异常, 而且能够进一步刻画其中的线性和环状构造、细微的局部纹理结构特征.该方法首先对地球物理和地球化学等网格数据进行二维矩阵的奇异值分解, 之后用左特征向量矩阵与右特征向量矩阵的直积构造一个正交完备基.地球物理和地球化学二维数据可以投影到该正交基上, 其投影系数是矩阵的奇异值.在该正交完备空间的某些子空间上对地球物理和地球化学等数据进行滤波.为了选择子空间, 本文定义了上述正交完备基中的能谱密度、能谱半径(或尺度) 与能量测度.在此基础上与空间域及频率域类比, 探讨了能量测度与能谱密度呈现分形(fractal和bifractal) 规律.利用分形关系的间断点, 设计分形奇异值重建算子, 实现对地球物理和地球化学异常的分解.以加拿大NovaScotia南部布格重力异常与As地球化学异常为例, 采用MSVD方法分解Au、Wu -Sn -U等已知矿有关的地球化学异常.发现重建异常能很好地用于解释已知矿点的分布规律.重建的地球化学异常显现了地球化学中的线状和环状异常; 重建的布格重力异常有效勾勒出原图中不易发现的纹理结构, 这些纹理结构可以合理地解释已知矿点在侵入岩体内及其周围的分布规律.应用实例表明, 该方法不仅可以从起因复杂的异常中区分出背景、异常场, 还可以识别代表了成矿源岩、流体、运移通道、赋存空间等异常因素引起的纹理、结构与构造特征.同时实现了GIS环境下交互可视化的MSVD处理与解释系统, 增强了地质异常定量分析的实用性与可操作性.      

勘查地球化学异常多尺度分析方法——以赣东北德兴矿集区为例

以赣东北德兴矿集区铜元素地球化学数据为例,提出了一种勘查地球化学异常多尺度分析的方法,内容包括:(1)采用具有平移不变性的平稳小波变换进行勘查地球化学异常的多尺度分解;(2)随着小波分解阶数的增大,在多尺度逼近累积频率—含量曲线图上,如果数据分布较均匀,没有出现明显的拐点,则该尺度逼近可以作为最大尺度的背景,该分解阶数为最大分解阶数;(3)各尺度的逼近不能直接代表该尺度的背景,各尺度的背景都是起伏的带而不是曲面,在多尺度细节累积频率—含量曲线图上,以拐点作为各尺度的异常下限;(4)大尺度的细节主要反映区域性的控岩控矿信息,而异常的形态和规模对研究控矿地质因素很重要,直接将各细节的正值作为异常;(5)小尺度的细节的正值分布过于复杂,分布规律不明显,因此一定要进行异常的提取,异常主要反映由矿和蚀变引起的局部变化,具有重要的找矿价值;(6)在各个尺度均表现为异常的区域具有找矿远景,特别是小尺度的局部异常,其不同尺度重叠区域是重要的找矿远景区。勘查地球化学异常的多尺度分析可获得多尺度的曲面背景和对应的异常,并可得到异常的精细结构,突出异常的局部变化特征,从而更有效地识别与矿有关的异常。