{"title":"Delineation of geochemical anomalies for mineral exploration using combining U-statistic method and fractal technique: U-N and U-A models","authors":"S. Ghannadpour, A. Hezarkhani","doi":"10.1080/25726838.2022.2041151","DOIUrl":null,"url":null,"abstract":"ABSTRACT\n In this study, by using the algorithm of the U-statistic and fractal methods and combining them with each other, a new combined method as U values fractal model (U-N and U-A) is introduced. Then, the proposed method is employed to determine the boundaries of background and anomalous populations. Results show that in U-N and U-A fractal models, the first fracture boundary is much clearer and more accurate than previous fractal models (C-N and C-A) in the same condition. In U-N model, due to the nature of the U method algorithm, there is a discontinuity as exact threshold between background and anomaly that in U-A model, this does not exist due to the homogenization of U values. In this method, the exact threshold between background and anomaly is determined by U-statistic method and by its combination with the fractal method, in each population, sub-populations are identified more accurately and simply than concentration fractal model.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/25726838.2022.2041151","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
ABSTRACT
In this study, by using the algorithm of the U-statistic and fractal methods and combining them with each other, a new combined method as U values fractal model (U-N and U-A) is introduced. Then, the proposed method is employed to determine the boundaries of background and anomalous populations. Results show that in U-N and U-A fractal models, the first fracture boundary is much clearer and more accurate than previous fractal models (C-N and C-A) in the same condition. In U-N model, due to the nature of the U method algorithm, there is a discontinuity as exact threshold between background and anomaly that in U-A model, this does not exist due to the homogenization of U values. In this method, the exact threshold between background and anomaly is determined by U-statistic method and by its combination with the fractal method, in each population, sub-populations are identified more accurately and simply than concentration fractal model.