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.
Highlights
Zoning of subsurface materials based on 3D modeling of quantitative description of rock mass quality is a powerful tool in different disciplines of civil and mining engineering paradigm.The geomechanical parameters logged from boreholes are explicitly able to provide sufficient information for the quality of rocks and their dominated region
A moving neighborhood is constructed based on the variogram prediction of rock quality designation (RQD)
Like just other geomechanical parameters prediction. In this the ordinary kriging dimensions, is used since respectively it is independent of the mean ranges andofset to 200 and algorithm, 150 for horizontal and vertical containing up to is non-additive means that the Aaverage ofneighborhood these kind measurements do on notthe follow the is linear and its stationary behavior moving constructed based variogram
Summary
Zoning of subsurface materials based on 3D modeling of quantitative description of rock mass quality is a powerful tool in different disciplines of civil and mining engineering paradigm.The geomechanical parameters logged from boreholes are explicitly able to provide sufficient information for the quality of rocks and their dominated region. A smoothing effect in the kriged results imposes difficulty in interpretation of the estimated map, it shows, for which a notable variance reduction in the conditional cumulative distribution This is associated with over- and under-estimation of low and high values, respectively [10], when one is comparing the interpolated map with original variability of the underlying dataset. Beside this reasonable criticism, some geomechanical parameters like RQD are non-additive and cannot be modeled by these types of linear interpolation methodologies [11]. In this context, there are some applications of different
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