Multivariate regression analysis of lithogeochemical data to model subsurface mineralization: a case study from the Sari Gunay epithermal gold deposit, NW Iran

Granian, Hamid; Tabatabaei, Sayed Gholam Hassan; Asadi, Hooshang H.; Carranza, Emmanuel John M.

doi:10.1016/j.gexplo.2014.10.009

Cited by 25 publications

(22 citation statements)

References 43 publications

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“…For uncovering relationships between independent and dependent variables, an appropriate statistical tool was introduced into geoscience by Granian et al (2015) which is called regression analyses. If dependent variables are called (Y ) and independent variables are called (x i ), the formula is…”

Section: Multivariate Regressionmentioning

confidence: 99%

“…Based on Granian et al (2015), the following criteria were utilized for the examination of the regression analysis:…”

Section: Multivariate Regressionmentioning

confidence: 99%

“…The coefficient of determination value which is called (R 2 ) should be tested (Granian et al, 2015). R 2 is presented as…”

Section: Multivariate Regressionmentioning

confidence: 99%

“…Given the fact that adding independent variables to the model will increase the R 2 value, the adjusted determination coefficient which is called (R 2 adj ) is presented as (Granian et al, 2015):…”

Section: Multivariate Regressionmentioning

confidence: 99%

“…Carranza, 2010a, b). Likewise, multivariate regression has been effectively utilized by Granian et al (2015) to display subsurface mineralization from lithogeochemical information. Granian et al (2015) used four types of multivariate regression models to depict significant surface geochemical anomalies indicating subsurface gold mineralization and utilizing borehole data as dependent variables and surface lithogeochemical data as independent variables.…”

Section: Introductionmentioning

confidence: 99%

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Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

et al. 2018

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Abstract. This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R 2 ) and adjusted determination coefficient (R 2 adj ), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

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Section: Multivariate Regressionmentioning

confidence: 99%

“…Based on Granian et al (2015), the following criteria were utilized for the examination of the regression analysis:…”

Section: Multivariate Regressionmentioning

confidence: 99%

“…The coefficient of determination value which is called (R 2 ) should be tested (Granian et al, 2015). R 2 is presented as…”

Section: Multivariate Regressionmentioning

confidence: 99%

Section: Multivariate Regressionmentioning

confidence: 99%

Section: Introductionmentioning