因子分析在地球化学数据处理中的应用

因子分析是一种多元统计分析方法,以往地质研究中常用其解决复杂的地质成因及矿化叠加问题。本文利用R型因子分析对岷县寨上-马坞调查区的1:20万水系沉积物样品数据进行处理,计算因子得分,圈定因子得分异常图,并与区内的各种地质作用相联系,取得了一定的效果;利用R-Q型因子分析对寨上及附近的原生晕样品进行分析,通过对矿体及构造带样品的分析,可以划分矿化类型;通过对地层样品的分析,可以划分地层并得出各地层的特征组合元素。因此利用因子分析可以为地球化学数据解释提供重要信息。     

形态相关系数及其在地球化学数据分析中的应用

地球化学数据元素间的相关分析具有重要意义。地球化学数据是一种不遵循正态分布的成分数据,其封闭特征存在挖掘和分析的困难和障碍,因此许多传统的统计方法不适合使用。主要通过元素值的趋势分析了地球化学元素之间的相关关系,并提出了形态相关系数的概念。该方法不需要数据服从正态分布,可以忽略闭包特征的影响。实验表明,该方法简单、稳定、准确,可以显示数据元素之间的关系。此外,方法计算过程消除了回溯,因此适用于大数据的实时和动态分析。     

RGMapGIS处理化探数据的优势及操作步骤

目前RGMapGIS系统在地质行业已被广泛应用。这里主要论述了利用RGMapGIS系统相对Suffer、MapGIS、GeoExplor软件处理地球化学数据和制作地球化学图件的优势,并简单地介绍了操作步骤,实现了简单、高效、准确的计算标准差、异常下限等参数和绘制等值线图、平剖图等,有助于对异常正确的解译分析,为下一步地质勘查工作确定了靶区。     

地球化学背景与地球化学基准

探讨了地球化学背景和地球化学基准的概念、含义和确定方法.认为随着地球化学的发展,地球化学背景的概念和含义都发生了很大的变化,由初期的注重其“量”到目前的关注其“质”,研究的目的不同,背景值的确定途径也不同.无论是地球化学背景还是地球化学基准,共同之处在于它们独特的参考功能,正是这种参照性质使地球化学背景和地球化学基准的研究在进行环境质量评价和环境立法时具有重要的理论意义和实用价值.     

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。     

勘查地球化学数据挖掘与弱异常识别

我国积累的大量高质量、多元素、多尺度的地球化学数据,为矿产勘查与环境评价提供了有效的数据支撑。如何对这些数据进行二次开发和再利用,提取有价值的地球化学异常信息并带动找矿突破,是缓解当前矿产资源短缺的重要途径之一。在覆盖区和深部的找矿实践中,由于矿体埋深和覆盖层的影响,往往在表生介质中形成弱小的地球化学异常,识别和评价弱小地球化学异常是当前勘查地球化学数据处理的重要方向之一。本文围绕地球化学异常信息的提取和评价,主要从以下几个方面讨论了相关的国内外研究进展和发展趋势:勘查地球化学数据处理与异常识别方法和模型,勘查地球化学数据闭合效应的影响及其解决方案,基于大数据和机器学习的勘查地球化学数据处理以及弱小地球化学异常的识别和评价。研究发现,在地质环境的约束下,基于大数据思维和机器学习相结合的方法,注重地球化学空间分布模式与已发现矿床的相关关系,同时使用所有地球化学变量能有效刻画具有非线性特征的地球化学空间分布模式,可识别出传统方法无法识别的异常,为开展地球化学空间模式识别与异常提取提供了新的途径。     

空间数据智能统计方法研究与软件实现

在进行空间数据统计分析的过程中,为了提高统计工作的效率,笔者采用C#+Arc Engine和数据库融合的开发模式,利用循环与数据库方法计算空间图斑数量、位置和面积等信息实现单属性和多属性自动分类统计,图斑质心八方向方位统计法确定图斑的空间位置,chart和word API方法实现地图数据的图表化和语言化表达。研究结果表明:将属性数据和空间数据分离可以实现空间数据准确高效地统计,循环和数据库方法的应用实现了空间数据的单属性和多属性分类统计;图斑质心八方向方位统计法的应用提高了空间统计的效率; chart和word API方法实现了属性数据统计图的自动生成以及地图数据报表化的自动生成与发布。     

Visualization of geochemical data on maps at the Geological Survey of Finland

Software for presentation of geochemical data on maps has been integrated into a user-friendly package, ALKEMIA, at the Geological Survey of Finland (GSF). ALKEMIA contains procedures for generating dot maps, colour surface maps, shaded relief maps, and vector images, and for interpolating and smoothing irregularly gridded data. Combining dots and colour surfaces provides a means of visually integrating up to three variables on a map, revealing the spatial relationships between them. The methods employed are described in detail, particularly the moving weighted median, which has been widely used at the GSF for interpolating and smoothing geochemical and other numerical data. Several geochemical and oncological atlases and series of maps have been produced with these methods.     

Application of universal kriging to geochemical prospecting data

In the 1:50,000 mineral-resource assessments for regions of different rock types, the application of universal kriging to process geochemical prospecting data, obtained from different sampling media, can provide much useful information for evaluating mineralization potential. The method has succeeded in effectively separating local anomalies from the regional background, objectively extracting useful information, and improving the analysis of the metallogenic and ore-controlling factors, thereby playing an important role in qualitative and quantitative predictions. In 1:50,000 mineral-resource assessment it is advantageous to use universal kriging in the processing of geochemical prospecting data because universal kriging has many advantages compared to other oreassessment methods.     

化探异常定量评价

通过对地球化学特征参数、地球化学异常特征值和综合地球化学异常指标的计算,得出了6个地球化学参数:剔除比率、变异系数、浓集比率、富集系数、矿化系数、单元素地球化学异常指标及综合地球化学异常指标,用于定量评价化探异常,以供探讨。     

如何利用Excel处理化探数据

利用Excel可以实现对化探数据多种项目的处理,如正态性检验;背景值及异常下限的确定;一元及多元回归分析;相关系数(R型聚类分析)以及一次趋势分析等,操作直观,目前在没有流行地质行业软件的情况下,不失为一种较为可取的选择。通过这种方法,可以较为简洁地实现对一定数量化探数据的处理,为地质找矿工作提供了化探方面的可靠依据。     

区域化探数据处理的几种分形方法

地球化学变量兼有随机性、确定性和区域结构性的特征,传统的数学方法难以精确地描述地球化学变量的空间分布规律,而新兴的分形几何是刻画空间不规则形体的一种比较有效的工具,且分维值可以表征空间不规则形体的确定性本质。基于分形原理,提出含量-总量法、空间分形插值法和分形趋势面法3种新的分形技术,应用于区域化探数据处理。在几个典型矿区应用后发现,上述分形方法不仅可以有效地模拟地球化学变量的空间分布特征,而且在保留原始有效异常信息的基础上可以更合理地区分地球化学变量的背景与异常,具有发现和强化弱异常信息的重要功效。     

确定岩性复杂区的地球化学背景与异常的方法

地球化学背景和异常的确定是化探找矿的关键。不论是水系沉积物还是土壤地球化学测量,岩性对许多元素背景值的影响都是十分显著的。在地质情况复杂的区域内,采用统一的异常下限值圈定异常是不合理的。根据样品的成矿元素与氧化物(SiO2)的相关关系,确定了岩性对成矿元素背景的影响。对受岩性影响显著的成矿元素,通过线性回归模型,以残差置信带确定元素异常的下限。实例研究证明,该方法在很大程度上消除了岩性变化对成矿元素背景的影响,能够有效地区分元素背景和异常。     

A spatial analysis method for geochemical anomaly separation

One purpose of using statistical methods in exploration geochemistry is to assist exploration geologists in separating anomalies from background. This always involves two types of negatively associated errors of misclassification: type I errors occur when samples with background levels are rejected as background; and type II errors occur when samples with anomalous values are accepted as background. A new spatial statistical approach is proposed to minimize errors of total misclassification using a moving average technique with variable window radius. This method has been applied for geochemical anomaly enhancement and recognition as demonstrated by a case study of Au and Au-associated data for 698 stream sediment samples in the Iskut River area, northwestern British Columbia. Similar results were obtained using the fractal concentration-area method on the same data. By employing spatial information in the analysis, the process of selecting anomalies becomes less subjective than in more traditional approaches.     

The problem of defining geochemical baselines. A case study of selected elements and geological materials in Finland

Although the term ‘geochemical baseline’ appears in the international geochemical mapping programmes IGCP 259 and 360, it has never been well defined. Several considerations relevant to such a definition are discussed. A geochemical baseline for an element refers to its natural variations in concentration in the surficial environment. Geochemical baselines were studied in Finland by comparing results from regional geochemical mapping programmes based on samples of till, clay and organic stream sediment. The geochemical background changes regionally with the basic geology and locally with the type and genesis of the overburden. Baseline concentrations depend on sample material collected, grain size and extraction method. In Finland, concentrations of potentially harmful elements tend to be higher in fine-grained marine and lacustrine sediments than in glacial till. Concentrations are also systematically higher in the < 0.06 mm fraction than in the < 2 mm size fraction of till samples. Only small proportions of the total heavy metal concentrations in Finnish marine clays are bioavailable. Geochemical baselines are needed for environmental legislation and political decision-making, especially in the assessment of contaminated soil. In many areas of Finland, natural concentrations of several heavy metals exceed the guide or limit values designated for contaminated soils. Thus baselines must always be verified in any assessment of sites for contamination.     

应用MAPGIS地形库管理整合1∶200000区域化探数据

利用1∶200 000地球化学数据,从整个成矿地球化学环境的角度出发,最大限度地开发、挖掘三十九种化学元素中每个元素以及元素组合所蕴含的矿化信息,为成矿远景区预测提供更加翔实的地球化学依据,是地球化学工作者的研究任务之一。MAPGIS功能强大,其地图库管理模块为多幅1∶200 000地球化学异常图的拼接提供了技术支持。这里论述了1∶200 000地球化学数据的特征和MAPGIS的地图库管理的功能,并记述了在实际工作中应用MAPGIS的地图库管理对多幅1∶200 000地球化学异常图的拼接,实现了对1∶200 000地球化学数据的另一种应用方式。     

Evaluation of some statistical methods of interpreting multi-element geochemical drainage data from New Brunswick

Univariate and multivariate statistical methods were evaluated using published multi-element stream sediment data from southwestern and northern New Brunswick. The statistical distributions of elements do not obey Ahrens' law of lognormality; eleven of thirteen elements investigated for the Bathurst-Jacquet River area are not lognormally distributed at the 0.05 level of significance. The distributions are positively skewed and leptokurtic and consist of aggregate populations which represent mineral deposits, bedrock, and many other physiographic factors; some of these populations are normally distributed. The efficiency of the Pearson correlation coefficient varied and was compared to nonparametric correlation. Various methods of factor analysis were evaluated and the structure of the factors was similar to the subjective groupings derived from the correlation matrices. Comparison of correlation coefficients and factor models derived from the log-transformed and untransformed Bathurst-Jacquet River data showed that background associations were enhanced by the log transformation at the expense of associations representing mineralization. Q-mode factor matrices could not be satisfactorily interpreted without recourse to the mapping of the factor loadings. The maps produced were inferior to simple concentration maps. An iterative technique was developed for discriminant analysis to refine the sample training groups representing mineralized and background terrain; repeated discriminant analysis after misclassified samples were eliminated altered the inherent character of the training groups. Trend surface analysis was found to give goodness of fits of the trend equations comparable to the fits expected from random numbers. The method was mathematically inappropriate for the type of data used. The goal of exploration geochemical statistical analysis should be to discriminate and sort populations representing mineralized and background populations by classification or filtering techniques.     

利用区域化探数据推断地质体空间分布

区域化探数据可以反映地层的空间分布,利用区域化探数据借助有效的数据挖掘方法,能够提取出其中包含的地质信息,对于覆盖区填图以及矿产勘查有重要意义,其中的关键问题是如何进行数据挖掘。随机森林算法是近年来热门的机器学习方法,本文应用随机森林算法结合非平衡数据集分类方法提出了一种新的化探数据挖掘方法,通过实例研究验证表明该方法准确率高,能够有效的提取出区域化探数据中的地质信息。     

区域化探数据在浅覆盖区地质填图中的应用方法研究

区域化探数据包含丰富的地质信息,可用于浅覆盖区区域地质填图。笔者系统研究了浅覆盖区水系沉积物化学成分与基岩化学成分的关系,利用水系沉积物氧化物成分,以区域岩石化学成分为约束,提出了基岩化学成分推断方法;根据水系沉积物与其矿物化学成分间质量平衡关系,提出了基岩矿物组成推断方法。在此基础上提出了地球化学推断地质图的编制方法,并在典型森林-沼泽浅覆盖区进行了试验,地质调查和钻探工程验证了该方法的有效性。充分利用区域化探资料,提取地质填图信息,是提高浅覆盖区地质填图质量的有效途径。     

利用自组织特征映射神经网络和K-means聚类算法挖掘区域化探数据中的地质信息

区域化探数据包含丰富的地质信息,从区域化探数据中挖掘出这些信息,对于区域地质研究具有重要意义.笔者提出了一种利用自组织特征映射网络和K-means聚类算法挖掘区域化探数据中地质信息的方法,将标准化之后的元素含量数据作为模型输入值,通过自组织神经网络进行聚类,再通过K-means算法进行二次聚类,从聚类结果中分析其中包含的地质信息.以英格兰西南部某区水系沉积物区域化探数据为例,进行实例研究以检验该方法的实际效果.实例结果表明:①利用该方法得出的聚类结果图很好地响应了地质体的空间分布,可用于推断地质体的分布特征;②地质信息隐藏在每个聚类类型的地球化学特征之中,通过对这些特征进行分析和解释,可以挖掘出其中所包含的信息;③基于SOM网络和K-means聚类的区域化探数据挖掘方法是一种有效的地质信息获取方法,对于传统区域地质研究可以起到补充和增强的作用.