地球化学场及其在隐伏矿体三维预测中的作用

本文从物理学"场论"的角度介绍了地球化学场的概念,并从场源、场作用及地球化学指标分布三个基本要素出发阐述了地球化学场的扩散、对流-扩散的动力学机制。首先,按照采样介质,将地球化学场分为原生的岩石地球化学场,以及次生的土壤、水系沉积物、水文和气体地球化学场,描述了组成各类地球化学场的要素。其次,将地球化学场的分析方法概括为静态的空间结构分析和动态的时空结构分析两种,提出要以场的动力学机制为基础,利用地球物理学中的正、反演理论来研究四维时空中地球化学场的发展和演化。最后,探讨了地球化学场时空结构分析与三维地学模拟两者之间彼此补充和相互验证的关系,三维地学模拟构建的场源及空间介质模型,为地球化学场的正、反演提供了初始条件,地球化学场反演的结果又可用来修正三维地质模型;探讨了地球化学场与大数据分析技术间的关系,即采用大数据的"数据驱动"的思路来挖掘其与多元地学数据之间的隐性联系,探索其与成矿过程的关联性。地球化学场与三维地学模拟、大数据分析技术的结合将为隐伏矿体三维预测中地下成矿物质的分布和演化提供依据。     

Semi-hierarchical correspondence cluster analysis and regional geochemical pattern recognition

Semi-hierarchical correspondence cluster analysis (SHCCA), firstly developed in this paper, extracts the main advantages of correspondence analysis, hierarchical and non-hierarchical cluster analysis, and unifies the R- and Q-mode cluster analysis of large data set. A systemic program to recognize the regional geochemical patterns is built up based on this method. With this program, the complex tasks for data interpretation can be achieved by simple processes, and important geochemical information can be displayed by a single diagram, i.e. the multivariate regional geochemical image. As one of the applied examples of this program, the regional geochemical pattern recognition for a shallow covered area around Tahe in Heilongjiang Province is introduced. The results show that many hidden geochemical patterns related to the lithologies, structures, ore-forming conditions and prospecting targets etc are revealed by the geochemical image, and that the main geochemical patterns are related with certain geological and gravitational patterns. By finding contrasts between geochemical patterns and geological or gravitational patterns, the SHCCA results assist the geological mapping in this area. Geochemical data obtained in Chinese regional geochemical exploration provides useful information regarding geology and minerals, and the method described in this paper provides a new way to examine this type of resource.     

基于关系数据库和WebGIS的中国东部地球化学科学数据库

如何高效管理、利用中国东部已有地球科学研究数据是急需解决的问题.在地球化学数据模型研究基础上, 应用关系数据库和WebGIS技术, 建立了中国东部地球化学科学数据库(ECGD).ECGD深度整合WebGIS技术, 研究者可利用ECGD基于Web对地球化学数据进行检索、分析、输出和空间可视化, 查询结果可以标注于基于WebGIS的网络地图, 生成分类、分级专题图, 并对查询结果进行地球化学图解等专业分析.ECGD是第一个基于Web, 集成了数据检索、空间数据可视化和数据分析功能的地球化学科学数据库, 为地学工作者提供了一个地球化学数据管理、共享、获取、交流的协作平台, 具有重要的实用价值.      

基于IFA的组合分析算法及其在矿产预测中的应用

矿产预测是寻找潜在矿产资源、增加后备储量的主要手段。地球化学信息作为直接找矿信息,在矿产预测中占据重要地位,对地球化学数据进行矿致异常的识别和评价是关键环节之一。地球化学数据分析的目的是从杂乱无章的原始数据中寻找并有效圈定化探组合异常。针对目前常用于组合异常分析的R型聚类分析和因子分析等传统算法对数据源限制较多、预测效果不理想等问题,将信号处理领域的盲源分离算法——独立因子分析(IFA)引入到矿产预测中,提出了基于IFA的组合分析算法,并选取广东汕头澄海区鸿沟山锡金多金属矿区地球化学数据作为研究对象。验证结果表明,基于IFA的组合分析结果更有利于解释矿产分布规律,圈定的化探组合异常更符合实际勘查结果。依据分析结果进行矿产预测,成功圈定出3个找矿靶区。     

Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization

Geochemical data are typical compositional data which should be opened prior to univariate and multivariate data analysis. In this study, a frequency-based method (robust principal component analysis, RPCA) and a frequency-space-based method (spectrum–area fractal model, S–A) are applied to explore the effects of the data closure problem and to study the integrated geochemical anomalies associated with polymetallic Cu mineralization using a stream sediment geochemical dataset collected from the Zhongteng district, Fujian Province (China). The results show that: (1) geochemical data should be opened prior to RPCA to avoid spurious correlation between variables; (2) geochemical pattern is a superimposition of multi-processes and should be decomposed; and (3) the S–A fractal model is a powerful tool for decomposing the mixed geochemical pattern.     

Single-element Anomaly Mapping from Stream Sediment Geochemical Landscapes Aided by Digital Terrain Analysis

The identification of anomalies within stream sediment geochemical data is one of the fastest developing areas in mineral exploration. The various means used to achieve this objective make use of either continuous or discrete field models of stream sediment geochemical data. To map anomalies in a discrete field model of such data, two corrections are required: background correction and downstream dilution correction. Topography and geomorphology are important factors in variations of element con...     

利用近地表化探资料研究油气藏的保存条件及埋藏深度

利用地面化探资料能够有效地圈定出化探异常,指出烃类垂向运移发生的范围。但对于油、气勘探的需要,这仅仅是最基本的,是远远不够的。本文通过对垂向化探资料及地面游离烃化探资料的分析,对利用地化动力学指标判断油、气藏的保存条件及埋藏深度进行了初步的探讨。     

基于MapGIS组件技术实现化探异常参数的计算

在Visual Basic环境中编写一个求化探异常参数的程序,程序首先运用MapGIS组件(COM)技术,构造了含有元素分析数据的点工作区,然后读入MapGIS异常区文件(.wp)。通过点对区的相交分析,提取出异常数据,进而对数据进行计算,求得异常参数。程序结果输出到EXCEL工作表中,非常实用。     

鄂尔多斯盆地北缘地球化学大数据样本优选分析

众所周知,地球化学数据携带有众多地质噪音,这些噪音严重影响地球化学数据信息的客观性与可靠性;对于地球化学大数据融合分析而言,确定样品的有效性及变量优选是滤除地质噪音、建立最优样本集合的必要性工作,因而在地球化学大数据处理分析前需首先进行大样本优选,从而更加客观、真实的揭示地球化学大数据信息及相关地质意义。本文以鄂尔多斯盆地北缘1∶20万地球化学土壤测量数据为例,考虑元素之间的地球化学亲和力与组合匹配关系,建立非线性大样本优选模型。具体做法是基于优选后的样品矩阵,将39个元素变量分解成若干独立因子向量,将最优独立因子向量作为元素组合,其向量各分量作为元素变量的权重,依权重大小进行变量优选;优选后的样本集合可以作为该区地球化学数据分析与信息识别的有效地学信息集合,运用这种集合可以有效开展鄂尔多斯盆地外围铀地球化学分析,并为盆地铀资源预测奠定基础。     

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.     

多目标地球化学勘查数据在油气化探中的应用 ———以宁夏盐池地区油气田为例

多目标地球化学勘查数据是在全国范围内对土壤的地球化学元素进行全面调查分析的大型数据库,若能将多目标地球化学勘查数据应用于油气化探中,将是一件非常有意义的研究.本文选择对盐池县油气田区域的多目标地球化学勘查数据进行分析,发现油田区土壤中部分元素的含量值出现异常,并且元素的异常与油气田的存在密切相关.通过对元素的平均含量、相关性及其对油气田的反映分析,发现异常元素Cu、Fe、Mn、V、Cl、S是该油区有效的化探指标.本研究有利于扩大多目标地球化学勘查数据的应用范围,使其不仅用于评价生态环境,还可以应用于油气资源的远景评价.     

Research and application of cluster and association analysis in geochemical data processing

For applications of data mining techniques in geosciences, through mining spatial databases which are constructed with geophysical and geochemical data measured in fields, critical knowledge, such as the spatial distribution of geological targets, the geophysical and geochemical characteristics of geological targets, the differentiation among the geological targets, and the relationship among geophysical and geochemical data, can be discovered. Due to the complexity of geophysical and geochemical data, traditional mining methods of cluster analysis and association analysis have limitations in processing complex data. In this paper, a clustering algorithm based on density and adaptive density-reachable is presented which has the ability to handle clusters of arbitrary shapes, sizes, and densities. For association analysis, mining the continuous attributes may reveal useful and interesting insights about the data objects in geoscientific applications. An approach for distance-based quantitative association analysis is presented in this paper. Experiments and applications indicate that the algorithm and approach are effective in real-world applications.     

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.     

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.     

1:50万等值线地球化学图研制及分析系统偏倚校正

利用1:20万地球化学勘查数据,编制小比例尺地球化学图,全国业已开展.经制图方法试验研究,本文提出了化探数据分析偏倚均值校正法,提出了1:50万等值线地球化学图编制方法技术,试验完成了1:50万和1:150万等值线地球化学图集.     

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LandSat 8遥感影像的净反射率值与地球化学因子之间的统计模型,并以此预测模型对测区进行了遥感地球化学异常的圈定。预测结果表明:建立的遥感地球化学统计模型高异常区与已知矿区范围基本吻合,通过提取的遥感地球化学异常不仅能表达化探常规异常的信息,还可以把其空间分布规律明晰出来,让某些局部弱异常得到增强。遥感地球化学异常得到了野外验证,说明利用地质资料建立的基于LandSat 8遥感地球化学预测模型比单一的遥感或化探异常有更好的预测效果。     

国际地球化学填图样品分析方法和数据对比

以中国和世界发达国家或地区(欧洲、北美、澳大利亚与日本)过去10年内完成的或目前正在开展的全球性或国家性地球化学填图项目为例,总结了国际地球化学样品分析技术的新进展,并以中国实验室与欧洲实验室的分析数据的对比结果,剖析国际地球化学填图样品分析技术面临的挑战。研究表明:分析组成地壳所有元素的构想已被越来越多国家性、全球性地球化学填图项目所采纳;中国是世界上唯一具有填图样品76元素分析能力的国家;高水平的分析实验室(欧洲与中国)取得的数据大部分可以实现对比,但仍有10余个元素的分析数据存在明显偏差。实现所有分析元素数据的全球可对比,应是今后国际地球化学填图样品分析技术的主要发展方向。     

地球化学数据的定和化及其在系统误差校正中的应用

地球化学数据间的系统误差,既影响了图幅的拼接,又因破坏了多元总体的相关结构而干扰到地球化学数据的统计分析。针对这一问题,这里分析了地球化学数据的定和特点,探讨了系统误差校正的定和化处理方法。同时,结合实例说明了该方法的实际应用效果,并验证了定和化方法的有效性。     

中国西北地区成矿元素区域地球化学特征

以中国西北地区陕西、甘肃、宁夏、青海、新疆五省(区)322万条区域地球化学数据为数据源,通过统计分析、空间分析、专家分析等手段,编制了地球化学系列图件,建立西北地区地球化学异常谱系。研究中首先统计了各地球化学区中W、Sn、Mo、Cu、Pb、Zn、Au、Ag、Ni共9种元素的富集程度,并以此为依据对各地球化学区进行排序;分别计算了各地球化学区和亚区W、Sn、Mo、Cu、Pb、Zn、Au、Ag、Ni等9种元素的异常显著度和综合异常显著度,据此对地球化学区和亚区进行排序。分析结果表明,秦岭地球化学区(Ⅳ-2)和(Ⅳ-2-4)小秦岭地球化学亚区分别为异常显著度最高的地球化学区和亚区,其中在小秦岭地球化学亚区中Mo异常显著度最高,异常规模最大,Au异常显著度次之。