An improved buffer analysis technique for model-based 3D mineral potential mapping and its application

Li, Nan; Bagas, Leon; Li, Xiaohui; Xiao, Keyan; Li, Ying; Ying, Lijuan; Song, Xiaogang

doi:10.1016/j.oregeorev.2015.12.002

Cited by 37 publications

(9 citation statements)

References 43 publications

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“…Many highly regarded studies have determined that the spatial distribution and morphology of geological features control orebody occurrence in epithermal deposits [3,43,45]. Thus, the spatial analysis of geological features is crucial for providing effective mineral exploration criteria [17,18,[46][47][48]. In this study, multi-spatial analysis methods were integrated to quantify their relative spatial control on gold mineralization and further generate a series of predictive maps by spatial distance analysis, slope analysis, and morphological undulation analysis.…”

Section: Spatial Analysis Of Geological Featuresmentioning

confidence: 99%

“…3D spatial analysis has been demonstrated as an effective approach for identifying the spatial features of geological objects in 3D space and extracting diverse potential ore-controlling features for 3D prospectivity modeling [17][18][19][20][21][22]. However, the spatial association between ore and ore-controlling features would be significantly more complicated with the expansion of geological features in space [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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3D Mineral Prospectivity Modeling for the Low-Sulfidation Epithermal Gold Deposit: A Case Study of the Axi Gold Deposit, Western Tianshan, NW China

et al. 2020

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The Axi low-sulfidation (LS) epithermal deposit in northwestern China is the result of geological controls on hydrothermal fluid flow through strike-slip faults. Such controls occur commonly in LS epithermal deposits worldwide, but unfortunately, these have not been quantitatively analyzed to determine their spatial relationships with gold distribution and further guide mineral prospecting. In this study, we conduct a 3D mineral prospectivity modeling approach for the Axi deposit involving 3D geological modeling, 3D spatial analysis, and prospectivity modeling. The spatial analysis of geometric features revealed the gold mineralization trends in convex segments (0–20 m) with a specific distance from fault 2, the lower interface of late volcanic phase, and the upper interface of phyllic alteration with steep slopes (>65°), implying that gold deposition was significantly controlled by the morphological characteristics and distance fields of geologic features. The present alteration–mineralization zone at Axi has a larger width in bending sites (sections No. 35–15 and No. 40–56) than elsewhere, indicating the location of two fluid conduits extending to depth. The prediction-area plots and receiver operating characteristic curves demonstrated that (genetic algorithm optimized support vector regression (GA-SVR)) outperformed multiple nonlinear regression and fuzzy weights-of-evidence, which was proposed as a robust method to solve complicated nonlinear and high-dimensional issues in prospectivity modeling. Our study manifests spatial controls of structure, host rock, and alteration on LS epithermal gold deposition, and highlights the capability of GA-SVR for identifying deposit-scale potential epithermal gold mineralization.

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Section: Spatial Analysis Of Geological Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D Mineral Prospectivity Modeling for the Low-Sulfidation Epithermal Gold Deposit: A Case Study of the Axi Gold Deposit, Western Tianshan, NW China

et al. 2020

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“…WofE approaches have recently made the step from 2D to 3D modelling (Li et al 2016;Payne et al 2015;Wang et al 2015;Xiao et al 2015;Yang et al 2017). The methods proposed in this paper do not depend on the spatial dimension, and could be applied to these 3D techniques.…”

Section: Overall Conclusionmentioning

confidence: 99%

Optimal Thresholding of Predictors in Mineral Prospectivity Analysis

et al. 2020

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Some methods for analysing mineral prospectivity, especially the Weights of Evidence technique, require the predictor variables to be binary values. When the original evidence data are numerical values, such as geochemical indices, they can be converted to binary values by thresholding. When the evidence layer is a spatial feature such as a geological fault system, it can be converted to a binary predictor by buffering at a suitable cutoff distance. This paper reviews methods for selecting the best threshold or cutoff value, and compares their performance. The review covers techniques which are well-known in prospectivity analysis as well as unfamiliar techniques borrowed from other literature. Methods include maximisation of the estimated contrast, Studentised contrast, χ 2 test statistic, Youden criterion, statistical likelihood, Akman-Raftery criterion, and curvature of the capture-efficiency curve. We identify connections between the different methods, and we highlight a common technical error in their application. Simulation experiments indicate that the Youden criterion has the best performance for selection of the threshold or cutoff value, assuming that a simple binary threshold relationship truly holds. If the relationship between predictor and prospectivity is more complicated, then the likelihood method is the most easily adaptable. The weights-of-evidence contrast performs poorly overall. These conclusions are supported by our analysis of data from the Murchison goldfields, Western Australia. We also propose a bootstrap method for calculating standard errors and confidence intervals for the location of the threshold.

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“…It is a common practice that the data to undergo integration must be fully or partially indicative of the mineral deposits sought, based on the customised exploration model for the area under search. Successful GIS-based mineral exploration research has been documented in several pieces of research (Silva et al , 2022; County, 2021; Verma et al , 2021; Li et al , 2016; Najafi et al , 2014; Xie et al , 2017; Xiong and Zuo, 2018; Yousefi and Carranza, 2017). The increasing computing power of GIS software now makes it possible to carry more sophisticated data integration, which utilises more information from traditional data sets such as remote sensing satellite imagery and geological and geophysical maps.…”

Section: Introductionmentioning

confidence: 99%

Application of GIS and remote sensing in mineral exploration: current and future perspectives

Tagwai

Jimoh

Shehu

et al. 2023

WJE

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Purpose This paper aims to give an oversight of what is being done by researchers in GIS and remote sensing (field) to explore minerals. The main objective of this review is to explore how GIS and remote sensing have been beneficial in identifying mineral deposits for easier and cost-effective mining. Design/methodology/approach The approach of this research used Web of Science to generate a database of published articles on the application of GIS and remote sensing techniques for mineral exploration. The literature was further digested, noting the main findings, adopted method, illustration and research scales. Findings When applied alone, each technique seems effective, but it is important to know that combining different methods is more effective in identifying ore deposits. Originality/value This paper also examined and provided possible solutions to both current and future perspective issues relating to the application of GIS and remote sensing to mineral exploration. The authors believe that the conclusions and recommendations drawn from case studies and literature review will be of great importance to geoscientists and policymakers.

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An improved buffer analysis technique for model-based 3D mineral potential mapping and its application

Cited by 37 publications

References 43 publications

3D Mineral Prospectivity Modeling for the Low-Sulfidation Epithermal Gold Deposit: A Case Study of the Axi Gold Deposit, Western Tianshan, NW China

3D Mineral Prospectivity Modeling for the Low-Sulfidation Epithermal Gold Deposit: A Case Study of the Axi Gold Deposit, Western Tianshan, NW China

Optimal Thresholding of Predictors in Mineral Prospectivity Analysis

Application of GIS and remote sensing in mineral exploration: current and future perspectives

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