Mining of Mineral Deposits

ISSN 2415-3443 (Online)

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Real-time deformation monitoring of open-pit mine dump sites using self-developed GNSS receivers: A case study of the Dong Cao Son dump site, Vietnam

Khai Cong Pham1, Hai Van Nguyen2

1Hanoi University of Mining and Geology, Hanoi, Vietnam

2Thuyloi University, Hanoi, Vietnam


Min. miner. depos. 2025, 19(3):120-131


https://doi.org/10.33271/mining19.03.120

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      ABSTRACT

      Purpose. This study aims to develop a real-time deformation monitoring system using GNSS/Continuously Operating Reference Station (CORS) technology for open-pit mine dump sites.

      Methods. The system comprises a single CORS station installed in Cam Pha City, Quang Ninh Province, Vietnam, utili-zing a Stonex GNSS receiver and a Trimble Zephyr 2 antenna. Components include a GNSS receiver based on the Trimble OEM BD970 module, monitoring control, and data processing software installed on a server computer for remote access. The real-time kinematic (RTK) method using CORS technology is employed for continuous monitoring at a frequency of 5 Hz, with data output in the standard NMEA format. The sliding window algorithm is applied to detect displacement occurrences and magnitudes. Performance and reliability are evaluated through two experiments with varying baseline lengths.

      Findings. The simulation experiment results show a maximum difference of 5 mm in horizontal displacement and 8 mm in vertical displacement when compared with measurements from a laser distance meter. The real-world experiment at the Dong Cao Son waste dump in Quang Ninh Province, Vietnam, confirms the system’s effectiveness and feasibility. Monitoring data are successfully transmitted to a host computer at Hanoi University of Mining and Geology.

      Originality. This study introduces a novel real-time deformation monitoring system based on GNSS/CORS technology, designed to ensure continuous, stable operation and real-time data processing over extended periods.

      Practical implications. The developed system provides an efficient, economical, and safe solution for real-time monitoring and early warning of deformations at open-pit dump sites, contributing to improved mining operations.

      Keywords: GNSS, CORS, mine dump deformation, open-pit mine, sliding window, Dong Cao Son dump


      REFERENCES

  1. Hu, X., Bürgmann, R., Lu, Z., Handwerger, A.L., Wang, T., & Miao, R. (2019). Mobility, thickness, and hydraulic diffusivity of the slow-moving Monroe landslide in California revealed by L-band satellite radar interferometry. Journal of Geophysical Research: Solid Earth, 124(7), 7504-7518. https://doi.org/10.1029/2019JB017560
  2. Hoy, M., Doan, C.B., Horpibulsuk, S., Suddeepong, A., Udomchai, A., Buritatum, A., Chaiwan, A., Doncommul, P., & Arulrajah, A. (2024). Investigation of a large-scale waste dump failure at the Mae Moh mine in Thailand. Engineering Geology, 329, 107400. https://doi.org/10.1016/j.enggeo.2023.107400
  3. Gao, S., Zhou, W., Shi, X., Cai, Q., Crusoe, G. E., Jr., Shu, J., & Huang, Y. (2017). Mechanical properties of material in a mine dump at the Shengli Surface Coal Mine, China. International Journal of Mining Science and Technology, 27(3), 545-550. https://doi.org/10.1016/j.ijmst.2017.03.014
  4. Khoa, V.V., & Takayama, S. (2018). Wireless sensor network in landslide monitoring system with remote data management. Measurement, 129, 214-229. https://doi.org/10.1016/j.measurement.2018.01.002
  5. Georgieva, K., Smarsly, K., König, M., & Law, K.H. (2012). An autonomous landslide monitoring system based on wireless sensor networks. Computing in Civil Engineering, 2012, 0019. https://doi.org/10.1061/9780784412343.0019
  6. Kuang, K.S.C., & Cao, Q. (2015). A low-cost, wireless chemiluminescence-based deformation sensor for soil movement and landslide monitoring. Structural Health Monitoring, 2015, 116. https://doi.org/10.12783/SHM2015/116
  7. Artese, S., & Perrelli, M. (2018). Monitoring a landslide with high accuracy by total station: A DTM-based model to correct for the atmospheric effects. Geosciences, 8(12), 457. https://doi.org/10.3390/geosciences8020046
  8. Gumilar, I., Fattah, A., Abidin, H.Z., Sadarviana, V., Putri, N.S.E., & Kristianto. (2017). Landslide monitoring using terrestrial laser scanner and robotic total station in Rancabali, West Java (Indonesia). AIP Conference Proceedings, 1857, 060001. https://doi.org/10.1063/1.4987084
  9. Liu, Y., Yao, X., Gu, Z., Zhou, Z., Liu, X., & Wei, S. (2024). Study on InSAR image fusion for improved visualization of active landslides in alpine valley areas: A case in the Batang Region, China. Computers & Geosciences, 186, 105481. https://doi.org/10.1016/j.cageo.2023.105481
  10. Strozzi, T., Klimeš, J., Frey, H., Caduff, R., Huggel, C., Wegmüller, U., & Rapre, A.C. (2018). Satellite SAR interferometry for the improved assessment of the state of activity of landslides: A case study from the Cordilleras of Peru. Remote Sensing of Environment, 217, 111-125. https://doi.org/10.1016/j.rse.2018.08.014
  11. Xiao, R., & He, X. (2013). Real-time landslide monitoring of Pubugou hydropower resettlement zone using continuous GPS. Natural Hazards, 69(3), 1647-1660. https://doi.org/10.1007/s11069-013-0768-x
  12. Abidin, H.Z., Andreas, H., Gamal, M., Surono, & Hendrasto, M. (2004). Studying landslide displacements in Megamendung (Indonesia) using GPS survey method. ITB Journal of Engineering Science, 36(2), 109-123. https://doi.org/10.5614/itbj.eng.sci.2004.36.2.2
  13. Su, M.-B., Chen, I.-H., & Liao, C.-H. (2009). Using TDR cables and GPS for landslide monitoring in high mountain area. Journal of Geotechnical and Geoenvironmental Engineering, 135(8), 1113-1121. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000047
  14. Gili, J.A., Corominas, J., & Rius, J. (2000). Using global positioning system techniques in landslide monitoring. Engineering Geology, 55(3), 167-192. https://doi.org/10.1016/S0013-7952(99)00122-1
  15. Zhao, W.Y., Zhang, M.Z., Ma, J., Han, B., Ye, S.Q., & Huang, Z. (2021). Application of CORS in landslide monitoring. IOP Conference Series: Earth and Environmental Science, 861, 042049. https://doi.org/10.1088/1755-1315/861/4/042049
  16. Shu, B., He, Y., Wang, L., Zhang, Q., Li, X., Qu, X., Huang, G., & Qu, W. (2023). Real-time high-precision landslide displacement monitoring based on a GNSS CORS network. Measurement, 217, 113056. https://doi.org/10.1016/j.measurement.2023.113056
  17. Wang, P., Liu, H., Nie, G., Yang, Z., Wu, J., Qian, C., & Shu, B. (2022). Performance evaluation of a real-time high-precision landslide displacement detection algorithm based on GNSS virtual reference station technology. Measurement, 199, 111457. https://doi.org/10.1016/j.measurement.2022.111457
  18. Benoit, L., Briole, P., Martin, O., Thom, C., Malet, J.-P., & Ulrich, P. (2015). Monitoring landslide displacements with the Geocube wireless network of low-cost GPS. Engineering Geology, 195, 111-121. https://doi.org/10.1016/j.enggeo.2015.05.003
  19. Cina, A., & Piras, M. (2015). Performance of low-cost GNSS receiver for landslides monitoring: Test and results. Geomatics, Natural Hazards and Risk, 6(5-7), 497-514. https://doi.org/10.1080/19475705.2014.889046
  20. Bellone, T., Dabove, P., Manzino, A.M., & Taglioretti, C. (2016). Real-time monitoring for fast deformations using GNSS low-cost receivers. Geomatics, Natural Hazards and Risk, 7(2), 458-470. https://doi.org/10.1080/19475705.2014.1003079
  21. Trimble. (n.d.). Zephyr 2 GNSS antenna [End-of-life product]. Retrieved from: https://oemgnss.trimble.com/en/products/end-of-life-products/zephyr2
  22. Stonex. (n.d.). SC2000 GNSS receiver. Retrieved from: https://www.stonex.it/project/sc2000-gnss-receiver/
  23. Pham, C.K., Tran, D.T., & Nguyen, V.H. (2021). Research and development of real-time high-precision GNSS receivers: A feasible application for surveying and mapping in Vietnam. Inżynieria Mineralna, 1(2(48)), 391-404. https://doi.org/10.29227/IM-2021-02-36
  24. National Marine Electronics Association. (n.d.). NMEA official website. Retrieved from: http://www.nmea.org
  25. Shen, N., Chen, L., & Chen, R. (2022). Displacement detection based on Bayesian inference from GNSS kinematic positioning for deformation monitoring. Mechanical Systems and Signal Processing, 167, 108570. https://doi.org/10.1016/j.ymssp.2021.108570
  26. Shehadeh, A., Alshboul, O., & Almasabha, G. (2024). Slope displacement detection in construction: An automated management algorithm for disaster prevention. Expert Systems with Applications, 237, 121505. https://doi.org/10.1016/j.eswa.2023.121505
  27. Pham, C.K., Tran, D.T., & Nguyen, V.H. (2020). GNSS/CORS-based technology for real-time monitoring of landslides on waste dump – A case study at the Deo Nai South Dump, Vietnam. Inżynieria Mineralna, 1(2(46)), 181-191. https://doi.org/10.29227/IM-2020-02-23
  28. Лицензия Creative Commons