Spatial mapping of the rock quality designation using multi-Gaussian Kriging method

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3 Citations (Scopus)

Abstract

The rock quality designation is an important input for the analysis and design of rock structures as reliable spatial modeling of the rock quality designation (RQD) can assist in designing and planning mines more efficiently. The aim of this paper is to model the spatial distribution of the RQD using the multi-Gaussian kriging approach as an alternative to the non-linear geostatistical technique which has shown some limitations. To this end, 470 RQD datasets were collected from 9 boreholes pertaining to the Gazestan ore deposit in Iran. The datasets were declustered then transformed into Gaussian distribution. To ensure the model spatial continuity, variogram analysis was first performed. The elevation 150 m with a grid of 5 m × 5 m × 5 m was selected to illustrate the methodology. Surface maps showing the RQD classes (very poor, poor, fair, good, and very good) with their associated probability were established. A cross-validation method was used to check the obtained model. The validation results indicated good prediction of the local variability. In addition, the associated uncertainty was quantified on the basis of the conditional distributions and the accuracy plot agreed with the overall results. It is concluded that the proposed model could be used to produce a reliable RQD map.

Original languageEnglish
Article number530
JournalMinerals
Volume8
Issue number11
DOIs
Publication statusPublished - Nov 15 2018

Fingerprint

rock quality designation
kriging
Rocks
Ore deposits
variogram
ore deposit
Gaussian distribution
method
Boreholes
Spatial distribution
borehole
spatial distribution
methodology
Planning
prediction
rock
modeling

Keywords

  • Multi-gaussian kriging
  • Probability
  • RQD
  • Spatial mapping

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology

Cite this

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title = "Spatial mapping of the rock quality designation using multi-Gaussian Kriging method",
abstract = "The rock quality designation is an important input for the analysis and design of rock structures as reliable spatial modeling of the rock quality designation (RQD) can assist in designing and planning mines more efficiently. The aim of this paper is to model the spatial distribution of the RQD using the multi-Gaussian kriging approach as an alternative to the non-linear geostatistical technique which has shown some limitations. To this end, 470 RQD datasets were collected from 9 boreholes pertaining to the Gazestan ore deposit in Iran. The datasets were declustered then transformed into Gaussian distribution. To ensure the model spatial continuity, variogram analysis was first performed. The elevation 150 m with a grid of 5 m × 5 m × 5 m was selected to illustrate the methodology. Surface maps showing the RQD classes (very poor, poor, fair, good, and very good) with their associated probability were established. A cross-validation method was used to check the obtained model. The validation results indicated good prediction of the local variability. In addition, the associated uncertainty was quantified on the basis of the conditional distributions and the accuracy plot agreed with the overall results. It is concluded that the proposed model could be used to produce a reliable RQD map.",
keywords = "Multi-gaussian kriging, Probability, RQD, Spatial mapping",
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AU - Adoko, Amoussou Coffi

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N2 - The rock quality designation is an important input for the analysis and design of rock structures as reliable spatial modeling of the rock quality designation (RQD) can assist in designing and planning mines more efficiently. The aim of this paper is to model the spatial distribution of the RQD using the multi-Gaussian kriging approach as an alternative to the non-linear geostatistical technique which has shown some limitations. To this end, 470 RQD datasets were collected from 9 boreholes pertaining to the Gazestan ore deposit in Iran. The datasets were declustered then transformed into Gaussian distribution. To ensure the model spatial continuity, variogram analysis was first performed. The elevation 150 m with a grid of 5 m × 5 m × 5 m was selected to illustrate the methodology. Surface maps showing the RQD classes (very poor, poor, fair, good, and very good) with their associated probability were established. A cross-validation method was used to check the obtained model. The validation results indicated good prediction of the local variability. In addition, the associated uncertainty was quantified on the basis of the conditional distributions and the accuracy plot agreed with the overall results. It is concluded that the proposed model could be used to produce a reliable RQD map.

AB - The rock quality designation is an important input for the analysis and design of rock structures as reliable spatial modeling of the rock quality designation (RQD) can assist in designing and planning mines more efficiently. The aim of this paper is to model the spatial distribution of the RQD using the multi-Gaussian kriging approach as an alternative to the non-linear geostatistical technique which has shown some limitations. To this end, 470 RQD datasets were collected from 9 boreholes pertaining to the Gazestan ore deposit in Iran. The datasets were declustered then transformed into Gaussian distribution. To ensure the model spatial continuity, variogram analysis was first performed. The elevation 150 m with a grid of 5 m × 5 m × 5 m was selected to illustrate the methodology. Surface maps showing the RQD classes (very poor, poor, fair, good, and very good) with their associated probability were established. A cross-validation method was used to check the obtained model. The validation results indicated good prediction of the local variability. In addition, the associated uncertainty was quantified on the basis of the conditional distributions and the accuracy plot agreed with the overall results. It is concluded that the proposed model could be used to produce a reliable RQD map.

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