Mineral potential mapping for Au and As using Gap statistic method in multivariate mode

Ghannadpour, Seyyed Saeed; Hezarkhani, Ardeshir

doi:10.1007/s13146-019-00546-8

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…The latter includes approaches which consider sampling point coordinates and their spatial relation in the estimation of anomalous areas. As the examples of structural or objective methods, techniques like discriminant analysis, fractal models, fuzzy clustering and U-spatial statistic can be noted (Kramar 1995; Cheng et al 2000; Goncalves et al 2001; Li et al 2003; Afzal et al 2010, 2011; Zuo 2011; Ghannadpour and Hezarkhani 2016a, 2016b, 2018, 2019, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Parkam is selected as case study and in order to evaluate the proposed combined U-N and U-A models because this area and its condition (sample type, their grid, alteration map, etc.) is very suitable for validation of methods performance based on previous studies (Ghannadpour et al 2015b, 2017a; Ghannadpour and Hezarkhani 2018, 2020). It should be noted that sampling in Parkam was superficial and the samples located in sediments were collected either from rock outcrops or from trenches having access to rock.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Delineation of geochemical anomalies for mineral exploration using combining U-statistic method and fractal technique: U-N and U-A models

Ghannadpour

Hezarkhani

2022

Applied Earth Science

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Delineation of geochemical anomalies for mineral exploration using combining U-statistic method and fractal technique: U-N and U-A models

Ghannadpour

Hezarkhani

2022

Applied Earth Science

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Intelligent geochemical exploration modeling using multiclass support vector machine and integration it with continuous genetic algorithm in Gonabad region, Khorasan Razavi, Iran

2021

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Application of the decision tree method to lithology identification of volcanic rocks-taking the Mesozoic in the Laizhouwan Sag as an example

Duan

Xie

et al. 2020

Sci Rep

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The decision tree method can be used to identify complex volcanic rock lithology by dividing lithology sample data layer by layer and establishing a tree structure classification model. Mesozoic volcanic strata are widely developed in the Bohai Bay Basin, the rock types are complex and diverse, and the logging response is irregular. Taking the D oilfield of the Laizhouwan Sag in the Bohai Bay Basin as an example, this study selects volcanic rocks with good development scales and single-layer thicknesses of more than 0.2 m as samples. Based on a comparison of various lithology identification methods and both coring and logging data, using the decision tree analysis method and the probability density characteristics of logging parameters, six logging parameters with good sensitivity to the response of the volcanic rocks of the above formation are selected (resistivity (RD), spontaneous potential (SP), density (ZDEN), natural gamma ray (GR), acoustic (DT), and compensated neutron correction (CNCF) curves), which are combined to form a lithology classifier with a tree structure similar to a flow chart. This method can clearly express the process and result of identifying volcanic rock lithology with each logging curve. Additionally, crossplots and imaging logging are used to identify the volcanic rock structure, and the core data are used to correct the identified lithology. A combination of conventional logging, imaging logging and the decision tree method is proposed to identify volcanic rock lithology, which substantially improves the accuracy of rock identification.

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Mineral potential mapping for Au and As using Gap statistic method in multivariate mode

Cited by 3 publications

References 33 publications

Delineation of geochemical anomalies for mineral exploration using combining U-statistic method and fractal technique: U-N and U-A models

Delineation of geochemical anomalies for mineral exploration using combining U-statistic method and fractal technique: U-N and U-A models

Intelligent geochemical exploration modeling using multiclass support vector machine and integration it with continuous genetic algorithm in Gonabad region, Khorasan Razavi, Iran

Application of the decision tree method to lithology identification of volcanic rocks-taking the Mesozoic in the Laizhouwan Sag as an example

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