IMPROVING THE METHODOLOGY OF GEODETIC MONITORING OF THE STATE OF THE EARTH’S SURFACE AND INSTRUMENT ARRAYS BASED ON DATA FROM UNMANNED AIRCRAFT SYSTEMS
Rubrics: GEODESY
Abstract and keywords
Abstract:
The article presents the improvement of the methodology of geodetic monitoring of the state of the earth’s surface and instrument arrays of open-pit mining on the basis of a comprehensive analysis of geospatial data obtained using unmanned aerial systems (UAS). The study was carried out using the example of the Sherubai Komir coal mine located in Central Kazakhstan, Karaganda region. In contrast to the previously applied practice with separate processing of models and expert interpretation, as well as traditional geodetic observations, a reproducible technological scheme is proposed. It integrates UAS data with total station and GNSS measurements in a single coordinate system and sets strict rules for combining multi-time models at control points. Aerial photography using UAS and integration of control point coordinates allowed us to obtain high-precision digital terrain models with a spatial resolution of 2.7 cm/ pixel. The analysis of multi-time models revealed areas of significant deformations, determined the directions of displacements and geometric transformations ofthe array; profile parameterization (angles, berm widths, linear and angular deformations) made it possible to translate geometric changes into stability calculations. Based on the data obtained, recommendations are formulated to ensure the stability ofthe sides (geometric adjustment, drainage and other engineering measures). The practical implementation of the proposed methodology makes it possible to increase the efficiency of geodetic monitoring and reduce the risks of accidents during open-pit mining.

Keywords:
geodetic monitoring, unmanned aircraft system, digital terrain model, reference and control points, combination of multi-time models, aerial photography, geospatial analysis, stability margin coefficient, total station, projection error
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References

1. Kosarev N.S., Kolesnikov A.A., Reznik A.V. i dr. Ispol'zovanie geoprostranstvennyh dannyh dlya ocenki sostoyaniya tehnogenno narushennyh zemel' // Fiziko-tehnicheskie problemy razrabotki poleznyh iskopaemyh. 2023. № 6. S. 190–197. DOIhttps://doi.org/10.15372/FTPRPI20230617. https://elibrary.ru/item.asp?id=58905362

2. Reznik A.V., Kolesnikov A.A., Kosarev N.S. i dr. Poluchenie i interpretaciya geoprostranstvennyh dannyh dlya postroeniya mul'timasshtabnoy cifrovoy modeli tehnogenno narushennyh territoriy // Gornyy zhurnal. 2024. № 11. S. 90–95. DOIhttps://doi.org/10.17580/gzh.2024.11.14. https://www.rudmet.ru/journal/2362/article/38862

3. Nurpeisova M.B., Bitimbaev M.Zh., Rysbekov K.B. i dr. Geodezicheskoe obosnovanie mednorudnogo rayona Saryarka // Zhurnal NAN RK. Seriya geologii i tehnicheskih nauk. 2020. T. 6, № 444. S. 194–202. DOIhttps://doi.org/10.32014/2020.2518-170X.147. https://doi.org/10.32014/2020.2518-170X.147

4. Ouyang Y., Feng T., Feng H., et al. Deformation Monitoring and Potential Risk Detection of In-Construction Dams Utilizing SBAS-InSAR Technology – A Case Study on the Datengxia Water Conservancy Hub // Water. 2024. Vol. 16. Iss. 7. P. 1025. DOIhttps://doi.org/10.3390/w16071025. https://doi.org/10.3390/w16071025

5. Nizametdinov F.K., Baryshnikov V.D., Oralbay A.O. Kentobe Pitwall Stability Estimation Using a Digital Geological-Geomechanical Model // Journal of Mining Science. 2022. Vol. 58. P. 896–902. DOIhttps://doi.org/10.1134/S1062739122060035. https://doi.org/10.1134/S1062739122060035

6. Haske B., Rudolph T., Bernsdorf B., et al. Innovative Environmental Monitoring Methods Using Multispectral UAV and Satellite Data // First Break. 2024. Vol. 42. Iss. 2. P. 41–47. DOIhttps://doi.org/10.3997/1365-2397.fb2024012. https://doi.org/10.3997/1365-2397.fb2024012

7. Gong C., Lei S., Bian Z., et al. Analysis of the Development of an Erosion Gully in an Open-Pit Coal Mine Dump During a Winter Freeze-Thaw Cycle by Using Low-Cost UAVs // Remote Sensing. 2019. Vol. 11. Iss. 11. P. 1356. DOIhttps://doi.org/10.3390/rs11111356. https://doi.org/10.3390/rs11111356

8. Bouguettaya A., Zarzour H., Taberkit A.M., et al. A Review on Early Wildfire Detection from Unmanned Aerial Vehicles Using Deep Learning-Based Computer Vision Algorithms // Signal Processing. 2022. Vol. 190. P. 108309. DOIhttps://doi.org/10.1016/j.sigpro.2021.108309. https://doi.org/10.1016/j.sigpro.2021.108309

9. Ismagilov R.I., Zaharov A.G., Badtiev B.P. i dr. Vnedrenie bespilotnyh letatel'nyh apparatov dlya operativnogo resheniya nauchno-proizvodstvennyh zadach v usloviyah Mihaylovskogo GOKa im. A.V. Varicheva // Gornaya promyshlennost'. 2020. № 3. S. 26–30. DOIhttps://doi.org/10.30686/1609-9192-2020-3-26-30. http://dx.doi.org/10.30686/1609-9192-2020-3-26-30

10. Samaei M., Stothard P., Shirani Faradonbeh R., et al. Mine Closure Surveillance and Feasibility of UAV–AI–MR Technology: A Review Study // Minerals. 2024. Vol. 14. Iss. 1. P. 110. DOIhttps://doi.org/10.3390/min14010110. https://doi.org/10.3390/min14010110

11. Pathak D., Kumar D., Dubey A. Drone for Surveillance // Economic Sciences. 2024. Vol. 20. No. 1. P. 32–37. DOIhttps://doi.org/10.69889/aw746p03. https://doi.org/10.69889/aw746p03

12. Shahmoradi J., Talebi E., Roghanchi P., et al. A Comprehensive Review of Applications of Drone Technology in the Mining Industry // Drones. 2020. Vol. 4. Iss. 3. P. 34. DOIhttps://doi.org/10.3390/drones4030034. https://doi.org/10.3390/drones4030034

13. Salvini R., Mastrorocco G., Seddaiu M., et al. The Use of an Unmanned Aerial Vehicle for Fracture Mapping Within a Marble Quarry (Carrara, Italy): Photogrammetry and Discrete Fracture Network Modelling // Geomatics, Natural Hazards and Risk. 2017. Vol. 8. Iss. 1. P. 34–52. DOIhttps://doi.org/10.1080/19475705.2016.1199053. https://doi.org/10.1080/19475705.2016.1199053

14. Yilmaz T., Berkan B., Ece A., et al. Açık maden sahalarında kazı sonrası zemin değişiminin izlenmesinde İHA-tabanlı RTK/PPK yönteminin kullanımı: Düzce-Tatlıdere taş ocağı örneği // Ormancılık Araştırma Dergisi. 2022. Cilt 9. S. 76–85. DOIhttps://doi.org/10.17568/ogmoad.1093694. https://doi.org/10.17568/ogmoad.1093694

15. Zhang H., Aldana-Jague E., Clapuyt F., et al. Evaluating the Potential of Post-Processing Kinematic (PPK) Georeferencing for UAV-Based Structure-From-Motion (SfM) Photogrammetry and Surface Change Detection // Earth Surface Dynamics. 2019. Vol. 7. Iss. 3. P. 807–827. DOIhttps://doi.org/10.5194/esurf-7-807-2019. https://doi.org/10.5194/esurf-7-807-2019

16. Taddia Y., Stecchi F., Pellegrinelli A. Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode // Drones. 2020. Vol. 4. Iss. 2. P. 9. DOIhttps://doi.org/10.3390/drones4020009. https://doi.org/10.3390/drones4020009

17. Lee E., Park S., Jang H., et al. Enhancement of Low-Cost UAV-Based Photogrammetric Point Cloud Using MMS Point Cloud and Oblique Images for 3D Urban Reconstruction // Measurement. 2024. Vol. 226. P. 114158. DOIhttps://doi.org/10.1016/j.measurement.2024.114158. https://doi.org/10.1016/j.measurement.2024.114158

18. Vicenç C., Pau M., Marc C., et al. Unmanned Aerial System Protocol for Quarry Restoration and Mineral Extraction Monitoring // Journal of Environmental Management. 2020. Vol. 270. P. 110717. DOIhttps://doi.org/10.1016/j.jenvman.2020.110717. https://doi.org/10.1016/j.jenvman.2020.110717

19. Famiglietti N.A., Cecere G., Grasso C., et al. A Test on the Potential of a Low Cost Unmanned Aerial Vehicle RTK/PPK Solution for Precision Positioning // Sensors. 2021. Vol. 21. Iss. 11. P. 3882. DOIhttps://doi.org/10.3390/s21113882. https://doi.org/10.3390/s21113882

20. Buzmakov S.A., Sannikov P.Y., Kuchin L.S., et al. The Use of Unmanned Aerial Photography for Interpreting the Technogenic Transformation of the Natural Environment During the Oilfield Operation // Journal of Mining Institute. 2023. Vol. 260. P. 180–193. DOIhttps://doi.org/10.31897/PMI.2023.22. https://doi.org/10.31897/PMI.2023.22

21. Vellemu E.C., Katonda V., Yapuwa H., et al. Using the Mavic 2 Pro Drone for Basic Water Quality Assessment // Scientific African. 2021. Vol. 14. P. e00979. DOIhttps://doi.org/10.1016/j.sciaf.2021.e00979. https://doi.org/10.1016/j.sciaf.2021.e00979

22. Turner D., Lucieer A., de Jong S.M. Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV) // Remote Sensing. 2015. Vol. 7. Iss. 2. P. 1736–1757. DOIhttps://doi.org/10.3390/rs70201736. https://doi.org/10.3390/rs70201736

23. Nizametdinov F.K., Nizametdinov N.F., Nizametdinov R.F. i dr. Instrumental'nyy kontrol' ustoychivosti rudnogo shtabelya na uchastke kuchnogo vyschelachivaniya // Gornyy zhurnal. 2022. № 2. S. 19–22. DOIhttps://doi.org/10.17580/gzh.2022.02.03. https://www.rudmet.ru/journal/2094/article/34977

24. Golser J., Steiner W. International and European Standards for Geotechnical Monitoring and Instrumentation = Internationale und europäische Normen für geotechnische Überwachung und Instrumentierung // Geomechanik und Tunnelbau. 2021. Vol. 14. Iss. 1. P. 63–77. DOIhttps://doi.org/10.1002/geot.202000047. https://doi.org/10.1002/geot.202000047

25. Tihonov A.A., Akmatov D.Zh. Obzor programm dlya obrabotki dannyh aerofotos'emki // Gornyy informacionno-analiticheskiy byulleten' (nauchno-tehnicheskiy zhurnal). 2018. № 12. S. 192–198. DOIhttps://doi.org/10.25018/0236-1493-2018-12-0-192-198. https://giab-online.ru/catalog/12641

26. Matyuha S.V. Iskusstvennyy intellekt v bespilotnyh aviacionnyh sistemah // Transportnoe delo v Rossii. 2022. № 1. S. 8–11. DOIhttps://doi.org/10.52375/20728689_2022_1_8. https://doi.org/10.52375/20728689_2022_1_8

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