Skip to main content
SpringerLink
Log in

Rock Mass Quality Prediction of Open-Pit Gold Mine Slope Based on the Kriging Interpolation Method

  • Original Paper
  • Published:
Geotechnical and Geological Engineering Aims and scope Submit manuscript

Abstract

Rock quality index (RQD) is a parameter which reflects the degree of rock fragmentation and is an important index for evaluating the stability and properties of rock masses in the field. Taking Chang Shanhao open pit gold mine in Inner Mongolia as the research object, RQD data from geological exploration drill core from the southwest stope are analyzed using the indicator Kriging interpolation method, which are then used as the sample points for variogram fitting in order to simulate the spatial variability of rock mass quality. Based on the geostatistics simulation prediction, the rock quality of the stope is predicted via interpolation, and a three-dimensional model corresponding to the RQD percentage and the risk of the slope rock mass is established to predict the quality of the slope rock mass of the stope, and a three-dimensional rock mass quality prediction diagram is constructed. Results show that the potential landslide area in the three-dimensional rock mass quality prediction map is consistent with field data and is consistent with the landslide disaster area revealed by excavations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  • Aalianvari A, Soltani-Mohammadi S, Rahemi Z (2018) Estimation of geomechanical parameters of tunnel route using geostatistical methods. Geomech Eng 14:453–458. https://doi.org/10.12989/gae.2018.14.5.453

    Article  Google Scholar 

  • Akbar DA (2012) Reserve estimation of central part of Choghart north anomaly iron ore deposit through ordinary kriging method. Int J Min Sci Technol 22(4):573–577

    Article  Google Scholar 

  • Amiri M, Lashkaripour GR, Ghabezloo S, Moghaddas NH, Tajareh MH (2019) 3D spatial model of Biot's effective stress coefficient using well logs, laboratory experiments, and geostatistical method in the Gachsaran oil field, southwest of Iran. Bull Eng Geol Environ 78:4633–4646. https://doi.org/10.1007/s10064-018-1423-2

    Article  Google Scholar 

  • Boergens E, Buhl S, Dettmering D et al (2016) The kriging method for combining multi-mission altimetry over the Mekong river. Living planet symposium.

  • Evrim S, Haluk A (2015) Geotechnical assessment and engineering classification of the Antalya tufa rock, southern Turkey. Eng Geol 197:211–224. https://doi.org/10.1016/j.enggeo.2015.08.029

    Article  Google Scholar 

  • Feng G, Kang Y, Wang XC et al (2020) Investigation on the failure characteristics and fracture classification of shale under Brazilian test conditions. Rock Mech Rock Eng. https://doi.org/10.1007/s00603-020-02110-6

    Article  Google Scholar 

  • Goovaerts P (2011) A coherent geostatistical approach for combining choropleth map and field data in the spatial interpolation of soil properties. Eur J Soil Sci 62:371–380. https://doi.org/10.1111/j.1365-2389.2011.01368

    Article  Google Scholar 

  • Gharaei-Manesh S, Fathzadeh A, Taghizadeh-Mehriardi R (2016) Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran. Cold Reg Sci Technol 122:26–35. https://doi.org/10.1016/j.coldregions.2015.11.004

    Article  Google Scholar 

  • Hengl T, Heuvelink GBM, Stein A (2004) A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 120:75–93. https://doi.org/10.1016/j.geoderma.2003.08.018

    Article  Google Scholar 

  • Jiang N, Wang CX, Pan HY et al (2020) Modeling study on the influence of the strip filling mining sequence on mining-induced failure. Energy Sci Eng. https://doi.org/10.1002/ese3.660

    Article  Google Scholar 

  • Kerry R, Goovaerts P, Smit IPJ, Ingram BR (2013) A comparison of multiple indicator kriging and area-to-point Poisson kriging for mapping patterns of herbivore species abundance in Kruger National Park, South Africa. Int J Geogr Inf Sci 27:47–67. https://doi.org/10.1080/13658816.2012.663917

    Article  Google Scholar 

  • Li XJ, Zhang ZY (2014) A combined indicator-ordinary Kriging method for estimating thicknesses of discontinuous geological strata. Rock Soil Mech 35:2881–2887

    Google Scholar 

  • Li YY, Zhang SC, Wen ZJ et al (2019) Energy conversion and fragment distribution characteristics of coal sample under uniaxial cyclic loading. J China Coal Soc 44(5):1411–1420

    Google Scholar 

  • Marache A, Riss J, Gentier S, Chiles JP (2002) Characterization and reconstruction of a rock fracture surface by geostatistics. Int J Numer Anal Met 26:873–896. https://doi.org/10.1002/nag.228

    Article  Google Scholar 

  • Nguyen XT, Nguyen BT, Do KP et al (2015) Spatial interpolation of meteorologic variables in vietnam using the Kriging method. J Inf Process Syst 11:134–147. https://doi.org/10.3745/JIPS.02.0016

    Article  Google Scholar 

  • Omri A, Hasna MO (2017) Modeling and performance analysis of 3-D heterogeneous networks with interference management. IEEE Commun Lett 21:1787–1790. https://doi.org/10.1109/lcomm.2017.2695609

    Article  Google Scholar 

  • Petronio L, Poletto F, Schleifer A (2007) Interface prediction ahead of the excavation front by the tunnel-seismic-while-drilling (TSWD) method. Geophysics 72:G39–G44. https://doi.org/10.1190/1.2740712

    Article  Google Scholar 

  • Polat N, Uysal M, Toprak AS (2015) An investigation of DEM generation process based on LiDAR data filtering, decimation, and interpolation methods for an urban area. Measurment 75:50–56. https://doi.org/10.1016/j.measurement.2015.08.008

    Article  Google Scholar 

  • Qin Z, Fu H, Chen X (2019) A study on altered granite meso-damage mechanisms due to water invasion-water loss cycles. Environ Geol 78:428. https://doi.org/10.1007/s12665-019-8426-6

    Article  Google Scholar 

  • Ren DZ, Zhou DS, Liu DK et al (2019) Formation mechanism of the Upper Triassic Yanchang Formation tight sandstone reservoir in Ordos Basin-Take Chang 6 reservoir in Jiyuan oil field as an example. J Pet Sci Eng 178:497–505

    Article  Google Scholar 

  • Ren FQ, Zhu C, He MC (2020) Moment tensor analysis of acoustic emissions for cracking mechanisms during schist strain burst. Rock Mech Rock Eng 53(1):153–170. https://doi.org/10.1007/s00603-019-01897-3

    Article  Google Scholar 

  • Sen Z (2009) Spatial modeling principles in earth sciences. Springer, Netherlands, Heidelberg, pp 1–351

    Book  Google Scholar 

  • Sharififar A, Sarmadian F, Malone BP, Minasny B (2019) Addressing the issue of digital mapping of soil classes with imbalanced class observations. Geoderma 350:84–92

    Article  Google Scholar 

  • Shi WZ, Li QQ, Zhu CQ (2005) Estimating the propagation error of DEM from higher-order interpolation algorithms. Int J Remote Sens 26:3069–3084. https://doi.org/10.1080/01431160500057905

    Article  Google Scholar 

  • Simon WH (1994) 3D Geoscience modeling: computer techniques for geological characterization. Earth-Sci Rev 46:85–90

    Google Scholar 

  • Soltani-Mohammadi S, Erhan Tercan A (2012) Constrained multiple indicator kriging using sequential quadratic programming. Comput Geosci 48:211–219. https://doi.org/10.1016/j.cageo.2012.01.003

    Article  Google Scholar 

  • Sun Q, Xue L, Wang YY (2009) Kriging method for parameter estimation and its application to geo-engineering. Rock Soil Mech 30:371–373

    Google Scholar 

  • Van Eldert J, Schunnesson H, Johansson D, Saiang D (2019) Application of measurement while drilling technology to predict rock mass quality and rock support for tunnelling. Rock Mech Rock Eng. https://doi.org/10.1007/s00603-019-01979-2

    Article  Google Scholar 

  • Villaescusa E, Potvin Y (2004) Ground support in mining and underground construction: proceedings of the fifth international symposium on ground support, Perth, Australia.

  • Viruete JE, Carbonell R, Marti D, Perez-Estaun A (2003) 3-D stochastic modeling and simulation of fault zones in the Albala granitic pluton, SW Iberian Variscan Massif. J Struct Geol 25:1487–1506

    Article  Google Scholar 

  • Wang J, Qiu PQ, Ning JG et al (2020a) A numerical study of the mining-induced energy redistribution in a coal seam adjacent to an extracted coal panel during longwall face mining: a case study. Energy Sci Eng 8(3):817–835

    Article  Google Scholar 

  • Wang X, Yuan W, Yan YT, Zhang X (2020b) Scale effect of mechanical properties of jointed rock mass: a numerical study based on particle flow code. Geomech Eng 21(3):259–268

    Google Scholar 

  • Wang YB, Wang D, Li ZX et al (2014) Research on 3D geological modeling based on shuffled frog leaping algorithm and Kriging. Syst Eng Theory Pract 34(011):2913–2920

    Google Scholar 

  • Yang J, Tao ZG, Li BL, GuiY LHF (2012) Stability assessment and feature analysis of slope in Nanfen Open Pit Iron Mine. Int J Min Sci Technol 22:329–333

    Article  Google Scholar 

  • You XH, Tang JS (2002) Research on horizontal push-shear in-situ test of soil and rock-mixture. Chin J Rock Mechan Eng 21:1537–1540

    Google Scholar 

Download references

Acknowledgements

This study was supported by the National Key Research and Development Project (Grant No.2016YFC0600901) and the Zhejiang Province Key R&D Projects (No. 2019C03104).

Author information

Authors and Affiliations

  1. State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology (Beijing), Beijing, 100083, China

    Zhigang Tao, Xuebin Cui, Xiaoming Sun, Jiong Wang, Kuiming Liu & Hong Chen

  2. School of Mechanics and Civil Engineering, China University of Mining & Technology (Beijing), Beijing, 100083, China

    Zhigang Tao, Xuebin Cui, Xiaoming Sun, Jiong Wang, Kuiming Liu & Hong Chen

Authors
  1. Zhigang Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuebin Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoming Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kuiming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhigang Tao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tao, Z., Cui, X., Sun, X. et al. Rock Mass Quality Prediction of Open-Pit Gold Mine Slope Based on the Kriging Interpolation Method. Geotech Geol Eng 38, 5851–5865 (2020). https://doi.org/10.1007/s10706-020-01397-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10706-020-01397-0

Keywords

Search

Navigation