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1.
Fractal modelling has been applied extensively as a means of characterizing the spatial distribution of geological phenomena
that display self-similarity at differing scales of measurement. A fractal distribution exists where the number of objects
exhibiting values larger than a specified magnitude displays a power-law dependence on that magnitude, and where this relationship
is scale-invariant. This paper shows that a number of distributions, including power-function, Pareto, log-normal and Zipf,
display fractal properties under certain conditions and that this may be used as the mathematical basis for developing fractal
models for data exhibiting such distributions. Population limits, derived from fractal modelling using a summation method,
are compared with those derived from more conventional probability plot modelling of stream sediment geochemical data from
north-eastern New South Wales. Despite some degree of subjectivity in determining the number of populations to use in the
models, both the fractal and probability plot modelling have assisted in isolating anomalous observations in the geochemical
data related to the occurrence of mineralisation or lithological differences between sub-catchments. Thresholds for the main
background populations determined by the fractal model are similar to those established using probability plot modelling,
however the summation method displays less capacity to separate out anomalous populations, especially where such populations
display extensive overlap. This suggests, in the geochemical data example provided, that subtle differences in the population
parameters may not significantly alter the fractal dimension. 相似文献
2.
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. 相似文献
3.
A Study of the Method for the Recognition of Anomalies in Geochemical Hydrocarbon Exploration 总被引：1，自引：0，他引：1
Zhang Liuping Doctoral Student China University of Geosciences Wuhan Liao Zebin North China Institute of Petroleum Exploration Development Renqiu 《中国地质大学学报(英文版)》1998,9(1)
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4.
Previous interpretations of surface-rock geochemical data from the sheeted-vein tin mineralization in the Emmaville district have been carried out using classical statistics. These investigations revealed low-contrast geochemical patterns of 3 to 5 ppm Sn, supported by 80 to 160 ppm F, block-average contours defining four of the six known mineral occurrences. Principal component scores for the association dominated by F-Li-Rb have defined the same four mineral occurrences. For the prospecting of similar deposits it is highly desirable to improve the data processing techniques to achieve more acceptable geochemical contrasts between anomalous and background levels. Minimum volume ellipsoid (MVE) estimation, a high-breakdown method (capable of accommodating up to 50% outliers) recently developed in robust statistics is applied to a subset of the data from the northeastern part of the Emmaville district. The anomalies related to mineralization in this part of the district are not as well developed compared to those in the west. The data set used in this study consists of 133 observations with 6 elements, namely Cu, Li, Rb, F, As and Sn.The detection of multivariate outliers (anomalous observations) by Mahalanobis distance calculation was carried out on the surface rock geochemical data. The robust Mahalanobis distances computed from MVE estimates of location and scatter shows little variation over background areas but are sharply enhanced over mineralization. In contrast, the usual Mahalanobis distances either fail to indicate the presence of mineralization altogether, or, at best, respond with feebly enhanced values that do not satisfactorily indicate the presence of mineralization.Graphical display of results from classical RQ-PCA performs poorly, revealing only 6 weakly anomalous observations related to mineral occurrences. Several additional observations from these occurrences have also gone undetected. On the other hand, results from MVE-robust RQ-mode principal component analysis show that the background observations cluster tightly within the 95% tolerance ellipse while the anomalous observations (related to mineral occurrences) are greatly enhanced and the variables that characterize them are clearly indicated. Results are consistent with those of robust Mahalanobis distance procedure; both techniques indicate essentially the same observations as being anomalous. 相似文献
5.
Yuwei Li 《Mathematical Geology》1985,17(3):243-265
It seems unreasonable to use one population to fit the distribution of an element, and then to determine a threshold to separate anomalous data from background data in an analysis of geochemical data. Statistically, anomaly, background, and other geological categories may be represented by different component populations overlapping one another. Therefore, anomaly, background, and other geological categories should be distinguished from one another by distributions rather than by thresholds. This paper uses a method of decomposition of mixtures to identify observed distributions of five elements, obtained in a geochemical reconnaissance of the Silver City-South Mountain region, Idaho, into component populations. Observations have been assigned to populations and mapped; finally, these populations have been interpreted leading to recognition of both mineralized belts and lithologic patterns.during the period in which this study was carried out. 相似文献
6.
《Chemie der Erde / Geochemistry》2016,76(4):491-499
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. 相似文献
7.
A reconnaissance rock geochemical survey defined the general area of the Mathiati pyrite deposit in Cyprus as anomalous; the results of a detailed study within a radius of two and one-half kilometres of the deposit are described in this paper.Individual samples from Mathiati are classified as anomalous or background using mathematical functions derived from the reconnaissance geochemical data. These functions are of two types: a discriminant function which is calculated by computer, and a determinative function which is derived graphically.The results from applying the functions to the Mathiati data are evaluated and compared. It is concluded that while none of the individual techniques described uniquely defines the mine, a combination of several techniques identifies the mine area as the primary target. 相似文献
8.
区域地球化学数据的归一化处理及应用 总被引：11，自引：0，他引：11
介绍了一种简单的勘查地球化学数据调整方法———归一化,该方法采用各批或各区块差异数据的平均值或中位数的衬值乘以参照值以获得元素的视含量,用于调整不同分析批次之间的系统误差,或不同背景区数据的差异;用2个实例详细介绍了该方法的应用效果。 相似文献
9.
A technique called SCORESUM was developed to display a maximum of multi-element geochemical information on a minimum number of maps for mineral assessment purposes. The technique can be done manually for a small analytical data set or can be done with a computer for a large data set. SCORESUM can be used with highly censored data and can also weight samples so as to minimize the chemical differences of diverse lithologies in different parts of a given study area.The full range of reported analyses for each element of interest in a data set is divided into four categories. Anomaly scores — values of O (background), 1 (weakly anomalous), 2 (moderately anomalous), and 3 (strongly anomalous) — are substituted for all of the analyses falling into each of the four categories. A group of elements based on known or suspected association in altered or mineralized areas is selected for study and the anomaly scores for these elements are summed for each sample site and then plotted on a map. Some of the results of geochemical studies conducted for mineral assessments in two areas are briefly described. The first area, the Mokelumne Wilderness and vicinity, is a relatively small and geologically simple one. The second, the Walker Lake 1° × 2° quadrangle, is a large area that has extremely complex geology and that contains a number of different mineral deposit environments. These two studies provide examples of how the SCORESUM technique has been used (1) to enhance relatively small but anomalous areas and (2) to delineate and rank areas containing geochemical signatures for specific suites of elements related to certain types of alteration or mineralization. 相似文献
10.
In large multi-element regional surveys statistically derived threshold levels of the form that define, for example, the top 2% of the data for each element as worthy of further investigation have led to the generation of inordinately large lists of geochemical samples for detailed study. This problem is compounded when a number of geological and secondary environments exists of sufficiently different character that separate thresholds should be estimated for each. Additionally, single-element thresholds for multi-element surveys can, in certain circumstances, lead to obviously out-of-character individuals not being recognized.Numerical approaches to the problem of anomaly recognition have commonly used a principal-component or regression analysis procedure as their basis. These, as indeed do all such approaches, have a common drawback in that the outliers being sought can distort the analysis being used to detect them. In addition, regression models have the further problem that there may be outliers in both the response and explanatory variables.A relatively simple approach would be to prepare a multivariate cumulative probability plot where each multi-element geochemical sample is represented as a single value. The resulting diagram would be interpreted much as a univariate probability plot where the presence of more than one straight-line segment is taken as evidence of multiple populations, and outliers as individuals or small groups are separated from the remaining data by gaps on the plot.Such a diagram may be prepared by plotting the rank-ordered values of the generalized or Mahalanobis distance, a multivariate distance measure, versus values of the chi-square statistic. This procedure is based on the covariance matrix of the data, a measure that underlies both principal-component and regression model approaches. In order to work effectively a statistically robust starting covariance matrix is essential.The procedure is described in detail with two examples, one a synthetic bivariate data set containing known outliers, and the other a small, well studied stream sediment data set from Norway extensively used in methodological comparison studies. The result of the procedure is to identify statistical outliers, which are candidates for interpretation as true geochemical anomalies, and to isolate a multi-element subset that is representative of the geochemical background. 相似文献
11.
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. 相似文献
12.
Samples from hazardous waste site investigations frequently come from two or more statistical populations. Assessment of background levels of contaminants can be a significant problem. This problem is being investigated at the U.S. Environmental Protection Agency's Environmental Monitoring Systems Laboratory in Las Vegas. This paper describes a statistical approach for assessing background levels from a dataset. The elevated values that may be associated with a plume or contaminated area of the site are separated from lower values that are assumed to represent background levels. It would be desirable to separate the two populations either spatially by Kriging the data or chronologically by a time series analysis, provided an adequate number of samples were properly collected in space and/or time. Unfortunately, quite often the data are too few in number or too improperly designed to support either spatial or time series analysis. Regulations typically call for nothing more than the mean and standard deviation of the background distribution. This paper provides a robust probabilistic approach for gaining this information from poorly collected data that are not suitable for above-mentioned alternative approaches. We assume that the site has some areas unaffected by the industrial activity, and that a subset of the given sample is from this clean part of the site. We can think of this multivariate data set as coming from two or more populations: the background population, and the contaminated populations (with varying degrees of contamination). Using robust M-estimators, we develop a procedure to classify the sample into component populations. We derive robust simultaneous confidence ellipsoids to establish background contamination levels. Some simulated as well as real examples from Superfund site investigations are included to illustrate these procedures. The method presented here is quite general and is suitable for many geological and biological applications. 相似文献
13.
浙江土壤地球化学基准值与环境背景值 总被引：7，自引：0，他引：7
以浙江省农业地质环境调查取得的区域地球化学资料(2002—2004年)为依据,遵循地球化学背景值的基本概念,根据成土母质类型将土壤地球化学基准值划分成13个统计单元,土壤环境背景值以浙北杭嘉湖与宁绍平原、浙东沿海温黄与温瑞平原、浙中金衢盆地区统计单元,在反复剔除异常数据后,获得了52种元素(氧化物)的平均值、标准离差和变异系数,为区域土壤环境质量标准的制订、土壤污染评价和治理修复提供了重要的地球化学依据。 相似文献
14.
Selective extraction procedures applied to geochemical prospecting in an area contaminated by old mine workings 总被引：1，自引：0，他引：1
F. Sondag 《Journal of Geochemical Exploration》1981,15(1-3)
A geochemical soil survey in the vicinity of the known ore body at Lontzen (Belgium) revealed numerous lead and/or zinc anomalies. Three soil traverses were selected in this area and examined for possible contamination. Two anomalous samples from one traverse were obviously contaminated (brick fragments). A sequential selective extraction procedure was applied to the soil samples, using a modification of the method of Gatehouse et al.Using this procedure, lead anomalies related to the probable extension of a known galena-sphalerite vein appear in every dissolution step. In contrast, in contaminated samples, only the final acid digestion produced anomalous values. One may thus suppose that contamination of the sample adds metal in the form of a resistant phase which is only dissolved by strong acid reagents. It should be noted that the contrast between anomalous and background values is highest for hydroxylamine hydrochloride and about the same for all other dissolution steps.The samples were also submitted to non-sequential selective extractions. The calculation of the difference between non-sequential and sequential extractions leads to the localization of two highly contrasted peaks which correspond exactly with the ore veins. 相似文献
15.
A spatial analysis method for geochemical anomaly separation 总被引：2，自引：0，他引：2
Qiuming Cheng F.P. Agterberg G.F. Bonham-Carter 《Journal of Geochemical Exploration》1996,56(3):183-195
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. 相似文献
16.
《Applied Geochemistry》1999,14(1):133-145
Three univariate geostatistical methods of estimation are applied to a geochemical data set. The studied methods are: ordinary kriging (cross-validation), factorial kriging, and indicator kriging. These techniques use the probabilistic and spatial behaviour of geochemical variables, giving a tool for identifying potential anomalous areas to locate mineralization. Ordinary kriging is easy to apply and to interpret the results. It has the advantage of using the same experimental grid points for its estimates, and no additional grid points are needed. Factorial kriging decomposes the raw variable into as many components as there are identified structures in the variogram. This, however, is a complex method and its application is more difficult than that of ordinary or indicator kriging. The main advantages of indicator kriging are that data are used by their rank order, being more robust about outlier values, and that the presentation of results is simple. Nevertheless, indicator kriging is incapable of separating anomalous values and the high values from the background, which have a behaviour different to the anomaly. In this work, the results of the application of these 3 kriging methods to a set of mineral exploration data obtained from a geochemical survey carried out in NW Spain are presented. This area is characterised by the presence of Au mineral occurrences. The kriging methods were applied to As, considered as a pathfinder of Au in this area. Numerical treatment of Au is not applicable, because it presents most values equal to the detection limit, and a series of extreme values. The results of the application of ordinary kriging, factorial kriging and indicator kriging to As make possible the location of a series of rich values, sited along a N–S shear zone, considered a structure related to the presence of Au. 相似文献
17.
多重地球化学背景下地球化学弱异常增强识别与信息提取 总被引：1，自引：0，他引：1
为对钦州湾-杭州湾成矿带(南段)庞西垌地区地球化学数据进行异常识别研究与信息提取,利用含量-面积法(C-A)得出庞西垌地区成矿主元素的异常下限,得到各元素异常分布图,并与已知矿(床)点进行叠加分析,发现已知矿(床)点与C-A法分析得到的异常区基本吻合,可根据该异常区预测未知矿床,从而为该研究区矿产资源潜力评价提供依据。为进一步从研究区复杂的地球化学背景中分离出与成矿有关的地球化学异常,采用分形滤波技术(S-A)提取致矿异常。研究表明,S-A法可在C-A法揭示的区域异常的基础上更深层次地提取出与矿化有关的局部异常用以反映研究区的多重地球化学背景,S-A法可有效地使弱异常增强进而提取出致矿异常,为庞西垌地区探寻隐伏矿体提供依据。 相似文献
18.
确定岩性复杂区的地球化学背景与异常的方法 总被引：3，自引：0，他引：3
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。 相似文献
19.
分形概念应用在地球科学中来刻画地质量和物体的自相似特征。研究表明分形模型常常提供有力工具来刻画地质量和物体的基本空间分布结构。本文提出了n维自仿射分形的检验与定量评定方法。通过实例，说明n维自仿射分形的方法在实际问题中的方法和步骤，并解释了分维数的实际意义。分维数足反映区域化变量在某方向变化程度的定量指标。该方法不仅适用于地球化学金元素和银元素数据，而且还适用于其他元素和地质数据，具有普遍的意义。 相似文献