U-spatial statistic data modeled on a probability diagram for investigation of mineralization phases and exploration of shear zone gold deposits

Ghavami-Riabi, R.; Seyedrahimi-Niaraq, Mirmahdi; Khalokakaie, Reza; Hazareh, M R

doi:10.1016/j.gexplo.2009.10.002

Cited by 27 publications

(7 citation statements)

References 17 publications

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“…And also, the Mo mineralization delineated by the U-statistic method is exactly associated with the phyllic alteration and is spatially conformable with the zone defined for it. Moreover, the proper application of this method could be seen in several studies (Cheng et al 1996(Cheng et al , 1997Cheng 1999;Ghavami-Riabi et al 2010;Darabi-Golestan et al 2013;Hezarkhani and Ghannadpour 2015).…”

Section: Introductionmentioning

confidence: 98%

“…Hence, anomaly separation methods from background can be divided into two groups which include non-structural and structural methods. Methods like discriminant analysis, U-spatial statistics, fractal technique and fuzzy clustering could be considered as examples of structural or objective methods (Cheng et al 1994;Kramar 1995;Cheng et al 2000;Goncalves et al 2001;Li et al 2003;Afzal et al 2010;Ghavami-Riabi et al 2010;Zuo 2011;Roshani et al 2013). U-statistic method is one of the structural methods.…”

Section: Introductionmentioning

confidence: 99%

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Introducing 3D U-statistic method for separating anomaly from background in exploration geochemical data with associated software development

Ghannadpour

Hezarkhani

2016

J Earth Syst Sci

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The U-statistic method is one of the most important structural methods to separate the anomaly from the background. It considers the location of samples and carries out the statistical analysis of the data without judging from a geochemical point of view and tries to separate subpopulations and determine anomalous areas. In the present study, to use U-statistic method in three-dimensional (3D) condition, U-statistic is applied on the grade of two ideal test examples, by considering sample Z values (elevation). So far, this is the first time that this method has been applied on a 3D condition. To evaluate the performance of 3D U-statistic method and in order to compare U-statistic with one non-structural method, the method of threshold assessment based on median and standard deviation (MSD method) is applied on the two example tests. Results show that the samples indicated by U-statistic method as anomalous are more regular and involve less dispersion than those indicated by the MSD method. So that, according to the location of anomalous samples, denser areas of them can be determined as promising zones. Moreover, results show that at a threshold of U = 0, the total error of misclassification for U-statistic method is much smaller than the total error of criteria ofx + n × s. Finally, 3D model of two test examples for separating anomaly from background using 3D U-statistic method is provided. The source code for a software program, which was developed in the MATLAB programming language in order to perform the calculations of the 3D U-spatial statistic method, is additionally provided. This software is compatible with all the geochemical varieties and can be used in similar exploration projects.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Introducing 3D U-statistic method for separating anomaly from background in exploration geochemical data with associated software development

Ghannadpour

Hezarkhani

2016

J Earth Syst Sci

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“…These haloes extend like the pod around the deposit. The shape and extent of expansion of these haloes depend on several factors, such as the form and amount of mineral deposit, the content of mineral reservoir, and the kind of host rock [9][10][11][12][13][14][15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

Comparison of U-spatial statistics method with Classical Statistics results in the determination of geochemical anomalies of Epithermal Gold in Khoshnameh area, Hashtjin, Iran

Seyedrahimi-Niaraq

2021

j. of geol. res.

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In this study, methods based on the distribution model (with and without personal opinion) were used for the separation of anomalous zones, which include two different methods of U-spatial statistics and mean plus values of standard deviation (). The primary purpose is to compare the results of these methods with each other. To increase the accuracy of comparison, regional geochemical data were used where occurrences and mineralization zones of epithermal gold have been introduced. The study area is part of the Hashtjin geological map, which is structurally part of the folded and thrust belt and part of the Alborz Tertiary magmatic complex. Samples were taken from secondary lithogeochemical environments. Au element data concerning epithermal gold reserves were used to investigate the efficacy of these two methods. In the U- spatial statistics method, and criteria were used to determine the threshold, and in the method, the element enrichment index of the region rock units was obtained with grouping these units. The anomalous areas were identified by, and criteria. Comparison of methods was made considering the position of discovered occurrences and the occurrences obtained from these methods, the flexibility of the methods in separating the anomalous zones, and the two-dimensional spatial correlation of the three elements As, Pb, and Ag with Au element. The ability of two methods to identify potential areas is acceptable. Among these methods, it seems the method with criteria has a high degree of flexibility in separating anomalous regions in the case of epithermal type gold deposits.

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“…To better understand the geological information associated with geochemical data, many researchers study the spatial distribution characteristics of the elements and the physical and chemical properties of the bare geologic body [23]. As a result, several quantitative geochemical methods, such as spatial statistics, the fractal technique, discriminant analysis, and fuzzy clustering [24][25][26][27][28], have been developed over the past few decades. These methods are associated with the density frequency of the element and are based on the perceptions of mineralization, which may lead to false anomalies that have nothing to do with mineralization [29].…”

Section: Introductionmentioning

confidence: 99%

A Fast Independent Component Analysis Algorithm for Geochemical Anomaly Detection and Its Application to Soil Geochemistry Data Processing

Liu

Guo

Wei

et al. 2014

Journal of Applied Mathematics

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A fast independent component analysis algorithm (FICAA) is introduced to process geochemical data for anomaly detection. In geochemical data processing, the geological significance of separated geochemical elements must be explicit. This requires that correlation coefficients be used to overcome the limitation of indeterminacy for the sequences of decomposed signals by the FICAA, so that the sequences of the decomposed signals can be correctly reflected. Meanwhile, the problem of indeterminacy in the scaling of the decomposed signals by the FICAA can be solved by the cumulative frequency method (CFM). To classify surface geochemical samples into true anomalies and false anomalies, assays of the 1 : 10 000 soil geochemical data in the area of Dachaidan in the Qinghai province of China are processed. The CFM and FICAA are used to detect the anomalies of Cu and Au. The results of this research demonstrate that the FICAA can demultiplex the mixed signals and achieve results similar to actual mineralization when 85%, 95%, and 98% are chosen as three levels of anomaly delineation. However, the traditional CFM failed to produce realistic results and has no significant use for prospecting indication. It is shown that application of the FICAA to geochemical data processing is effective.

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U-spatial statistic data modeled on a probability diagram for investigation of mineralization phases and exploration of shear zone gold deposits

Cited by 27 publications

References 17 publications

Introducing 3D U-statistic method for separating anomaly from background in exploration geochemical data with associated software development

Introducing 3D U-statistic method for separating anomaly from background in exploration geochemical data with associated software development

Comparison of U-spatial statistics method with Classical Statistics results in the determination of geochemical anomalies of Epithermal Gold in Khoshnameh area, Hashtjin, Iran

A Fast Independent Component Analysis Algorithm for Geochemical Anomaly Detection and Its Application to Soil Geochemistry Data Processing

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