Spatial Mapping of the Rock Quality Designation Using Multi-Gaussian Kriging Method

Madani, Nasser; Yağız, Saffet; Adoko, Amoussou Coffi

doi:10.3390/min8110530

Cited by 9 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimated model contributes to the knowledge of the spatial variation of the geomechanical characteristics, which can help, for example, to design the future slope dimensions according to the different local characteristics (Lana et al, 2010;Madani et al, 2018;Maninoni, 2003;Pereira et al, 2017).…”

Section: Geomechanical Classification Systemsmentioning

confidence: 99%

Indicator kriging applied to RMR geomechanical modelling in a Pb-Zn deposit case study

et al. 2021

View full text Add to dashboard Cite

The opening of a mining enterprise requires knowledge of the geomechanical characteristics of the mineral deposit, which will be worked according to the use of parameters of stability and/or resistance of the material, as well as the discontinuities present in the rock. In this study, the Rock Mass Rating methodology was used to propose a geomechanical model in a lead and zinc deposit hosted in the sedimentary rocks of Camaquã Basin, Brazil. A geological model was created using the analysis of 50 drill holes with their geological descriptions, grouped into five lithological units, from base to top: rhythmite, lower sandstone, conglomerate, upper sandstone, and ore. The samples were regularized in composites of three meters in length, and then categorized into the five classes of the RMR system. Classes were estimated for each lithological unit using indicator kriging to obtain a model with the probability of each estimated block belonging to each RMR class, and thus the predominant class probabilities of each block. While it was possible to observe a predominance of class II (good rock) in the conglomerate, lower sandstone and ore, in the upper sandstone and rhythmite class III predominates (fair rock), and a few blocks were classified in class IV (poor rock) and V (very poor rock). Although the geomechanical model demonstrates the quality of the rock mass in general, being indispensable to more detailed studies, it is believed that the model allows greater safety in the classification of the rock mass and later use for slope stability and pit design.

show abstract

Section: Geomechanical Classification Systemsmentioning

confidence: 99%

Indicator kriging applied to RMR geomechanical modelling in a Pb-Zn deposit case study

et al. 2021

View full text Add to dashboard Cite

show abstract

“…For this purpose, areas with more than 10% CaO and less than 40% SiO 2 define ore zones, and areas with less than 10% CaO and more than 40% SiO 2 introduce waste zones based on mining excavation destination. Before initiating the modelling process, it might be of interest to calculate two global recovery functions, fraction of recoverable ore above or below the cutoff and mean grade above or below the cutoff [28,42]. These two parameters are calculated by bivariate cumulative distribution functions computed over CaO and SiO 2 as follows:…”

Section: Exploratory Data Analysis In Limestone Depositmentioning

confidence: 99%

Stochastic Modeling of Chemical Compounds in a Limestone Deposit by Unlocking the Complexity in Bivariate Relationships

Battalgazy

Madani

2019

Minerals

View full text Add to dashboard Cite

Modeling multivariate variables with complexity in a cross-correlation structure is always applicable to mineral resource evaluation and exploration in multi-element deposits. However, the geostatistical algorithm for such modeling is usually challenging. In this respect, projection pursuit multivariate transform (PPMT), which can successfully handle the complexity of interest in bivariate relationships, may be particularly useful. This work presents an algorithm for combining projection pursuit multivariate transform (PPMT) with a conventional (co)-simulation technique where spatial dependency among variables can be defined by a linear model of co-regionalization (LMC). This algorithm is examined by one real case study in a limestone deposit in the south of Kazakhstan, in which four chemical compounds (CaO, Al2O3, Fe2O3, and SiO2) with complexity in bivariate relationships are analyzed and 100 realizations are produced for each variable. To show the effectiveness of the proposed algorithm, the outputs (realizations) are statistically examined and the results show that this methodology is legitimate for reproduction of original mean, variance, and complex cross-correlation among the variables and can be employed for further processes. Then, the applicability of the concept is demonstrated on a workflow to classify this limestone deposit as measured, indicated, or inferred based on Joint Ore Reserves Committee (JORC) code. The categorization is carried out based on two zone definitions, geological, and mining units.

show abstract

“…An ordinary kriging technique has also been used to generate top sand thickness and seabed surface [19]. The prediction of soil properties from terrain attributes using various forms of the kriging regression analysis technique has been reported to perform well [20,21]. Kriging has been evaluated as an unbiased and least error-prone optimum reserve estimation method to reduce or avoid ambiguities in plotting geological cross-sections on the basis of engineering geological properties and data from exploratory holes [22].…”

Section: Introductionmentioning

confidence: 99%

Regional Geotechnical Mapping Employing Kriging on Electronic Geodatabase

Arshid

Kamal

2020

Applied Sciences

View full text Add to dashboard Cite

A regional geotechnical map was developed by employing kriging using spatial and s geostatistical analysis tools. Many studies have been carried out in the field of topography, digital elevation modeling, agriculture, geological, crop, and precipitation mapping. However, no significant contribution to the development of geotechnical mapping has been made. For the appraisal of a geotechnical map, extensive field explorations were carried out throughout the geotechnically diversified plateau spread over an area of approximately 23,000 km2. In total, 450 soil samples were collected from 75 data stations to determine requisite index properties and soil classification for the subsequent allowable bearing capacity evaluation. The formatted test results, along with associated geospatial information, were uploaded to ArcMap, which created an initial input electronic database. The kriging technique of geostatistical analysis was determined to be more feasible for generating a geotechnical map. The developed map represents the distribution of soil in the region as per the engineering classification system, allowable bearing capacity, and American Association of State Highway and Transportation Officials (AASHTO) subgrade rating for 1.5-, 3.0-, and 4.5-m depths. The accuracy of the maps generated using kriging interpolation technique under spatial analyst tools was verified by comparing the values in the generated surface with the actual values measured at randomly selected validation points. The database was primarily created for the appraisal of geotechnical maps and can also be used for preliminary geotechnical investigations, which saves the cost of soil investigations. In addition, this approach allows establishing useful correlations among the geotechnical properties of soil.

show abstract

Spatial Mapping of the Rock Quality Designation Using Multi-Gaussian Kriging Method

Cited by 9 publications

References 34 publications

Indicator kriging applied to RMR geomechanical modelling in a Pb-Zn deposit case study

Indicator kriging applied to RMR geomechanical modelling in a Pb-Zn deposit case study

Stochastic Modeling of Chemical Compounds in a Limestone Deposit by Unlocking the Complexity in Bivariate Relationships

Regional Geotechnical Mapping Employing Kriging on Electronic Geodatabase

Contact Info

Product

Resources

About