Processing and calibration methodology of telemetry data for the process of roller-bit drilling of blast wells

Drilling telemetry systems are an effective tool in the mining industry for operational characterization of the massif and optimization of drilling and blasting operations, providing more detailed and cost-effective geological information compared to traditional methods. Despite successful implementations, there are unresolved issues in data interpretation related to the variability of geology and differences in equipment (types of systems, drilling rigs), which complicates the universal application of methods. Data interpretation methods often require individual calibration for a specific deposit/site taking into account the geological structure and the measurement systems used, which is confirmed by the example of Kuzbass open-pit mines. The article proposes an approach to further clarifying geological information by comparing drilling data (especially the estimated drilling energy intensity) for contour or other available blastholes with the actual geological structure observed on the bench slope after the blast. For detailed mapping of the slope geology and precise binding of geological boundaries to borehole depths, video recording and photogrammetry using UAVs are effectively used, followed by comparison with telemetry data. The use of different models of drilling rigs leads to a discrepancy between the measured parameters and the estimated drilling specific energy intensity in the same geological conditions due to design features. It is necessary to calibrate data from different rigs relative to the reference one. Two approaches are proposed: equalization of the final specific energy intensity or search for correction factors for individual telemetry parameters in areas with homogeneous geology. The use of calibration coefficients allows data from different rigs to be converted to a single reference system, ensuring comparability of specific energy intensity indicators and the reliability of subsequent geological interpretation and construction of block models.

Keywords: drilling telemetry, drilling specific energy intensity, blastholes, drilling rig calibration.
For citation:

Isheyskiy V. A., Marinin M. A., Peters K. I. Processing and calibration methodology of telemetry data for the process of roller-bit drilling of blast wells. MIAB. Mining Inf. Anal. Bull. 2025;(11-1):5—22. [In Russ]. DOI: 10.25018/0236_1493_2025_111_0_5.

Acknowledgements:
Issue number: 11-1
Year: 2025
Page number: 5-22
ISBN: 0236-1493
UDK: 622.235.5
DOI: 10.25018/0236_1493_2025_111_0_5
Article receipt date: 14.08.2025
Date of review receipt: 10.09.2025
Date of the editorial board′s decision on the article′s publishing: 10.10.2025
About authors:

Isheyskiy V. A.1, Cand. Sci. (Eng.), Assistant Professor, e-mail: Isheyskiy_VA@pers.spmi. ru, ORCID ID: 0000-0003-1007-6562,
Marinin M. A.1, Cand. Sci. (Eng.), Assistant Professor, Head of the Blasting department, e-mail: marinin_ma@pers.spmi.ru, ORCID ID: 0000-0002-5575-9343;
Peters K. I.2, Deputy Director for Prospective Development of Drilling and Blasting Technologies, e-mail: k.peters@vgroup.one;
1 Empress Catherine II Saint-Petersburg Mining University, 199106, Saint-Petersburg, Russia;
2 LLC «VZRYV GRUPP» 650000, Kemerovo, Russia.

For contacts:

Isheyskiy VA., Isheyskiy_VA@pers.spmi.ru.

Bibliography:

1. Isheyskiy V., Sanchidrián, J. A. Prospects of Applying MWD Technology for Quality Management of Drilling and Blasting Operations at Mining Enterprises. Minerals. 2020, vol. 10, 925. https://doi.org/10.3390/min10100925.
2. Aleksandrov V. G. Experimental test of fractional-energy approach adequacy for the parameters of drilling and blasting according to the telemetric data. Izvestiya vysshikh uchebnykh zavedenii. Gornyi zhurnal Minerals and Mining Engineering. 2024, no. 4, pp. 97–112 [In Russ]. DOI: 10.21440/0536-1028-2024-4-97−112. 
3. Navarro J., Sanchidrian J. A.,Segarra P., Castedo R., Costamagna E., Lopez L. M. Detection of potential overbreak zones in tunnel blasting from MWD data. Tunnelling and Underground Space Technology. 2018, vol. 82, pp. 504–516.
4. Navarro J., Sanchidrian J. A., Segarra P., Castedo R., Paredes C., Lopez L. M. On the mutual relations of drill monitoring variables and the drill control system in tunneling operations. Tunnelling and Underground Space Technology. 2018, vol. 72, pp. 294–304. 
5. Khorzoughi M. B., Hall R., Apel D. Rock fracture density characterization using measurement while drilling (MWD) techniques. International Journal of Mining Science and Technology. 2018, vol. 28(6), pp. 859–864.
6. Akyildiz O., Basarir H., Ellefmo S. L. The development of a lithol-ogy prediction model using measurement while drilling data ina quartzite quarry. Int J Min Reclam Environ. 2024. https://doi.org/10.1080/17480930.2024.2362577. 
7. Opanasenko P. I., Isaichenkov A. B. The Blast maker software/hardware complex use in drilling and-blasting designing in Tugnuisky open pit mine. MIAB. Mining Informational and Analytical Bulletin. 2013, no. S2, pp. 38–57.
8. Tangaev I. A. Burimost’ i vzryvaemost’ gornyh porod, Moscow, Nedra, 1978, 184 p. [In Russ]. 
9. Kriukov G. M. Physics of rock breaking when drilling and blasting. Vol. 1. Moscow, Gornaia kniga Publishing, 2006. [In Russ].
10. Teale R. The concept of specific energy in rock drilling. International Journal of Rock Mechanics and Mining Sciences. 1965, p. 245. DOI: 10.1016/0148−9062(65)90022−7.
11. Goldstein D. M., Aldrich C., O’Connor L. A review of orebody knowledge enhancement using machine learning on open-pit mine measure-while-drilling data. Mach Learn Knowl Extr. 2024, vol. 6(2), pp. 1343–1360. https://doi.org/10.3390/make6020063. 
12. Akyildiz O., Basarir H., Vezhapparambu V. S., Ellefmo S. MWD data-based marble quality class prediction models using ML algo rithms. Math Geosci. 2023, vol. 55(8), pp. 1059–1074. https://doi.org/10.1007/ s11004-023-10061-1. 
13. Silversides K. L., Melkumyan A. Machine learning for classifica tion of stratified geology from MWD data. Ore Geol Revi. 2022, vol. 142. https://doi.org/10.1016/j.oregeorev.2022.104737. 
14. Komadja G. C., Westman E., Rana A. et al. Predicting rock mass strength from drilling data using synergistic unsupervised and supervised machine learning approaches. Earth Sci Inform. 2025, vol. 18, 325. https://doi.org/10.1007/s12145-025-01837-6. 
15. Ghosh R. Assessment of rock mass quality and its effects on chargeability using drill monitoring technique. Doctoral thesis, Luleå: University of Technology, Sweden, 2017.
16. Navarro M. J. The use of measure while drilling for rock mass characterization and damage assessment in blasting. Doctoral Thesis. Universidad Politécnica de Madrid E. T. S. I. Minas y Energía (UPM), Spain, 2018.
17. Kovalevsky V. N., Mysin A. V., Sushkova V. I. Theoretical aspects of block stone blasting method. Mining Science and Technology (Russia). 2024, no. 9(2), pp. 97–104. [In Russ]. https://doi.org/10.17073/2500-0632-2023-12−187. 
18. Afanasev P. I. Analysis of shock wave parameters at the explosive cavity wall during refraction of detonation waves through the air and water. Sustainable Development of Mountain Territories. 2023, no. 15(3), pp. 505—515. [In Russ]. DOI: 10.21177/1998-4502-2023-15−3-505−515. 
19. Dolzhikov V. V., Ryadinsky D. E., Yakovlev A. A. Influence of deceleration intervals on the amplitudes of stress waves during the explosion of a system of borehole charges. MIAB. Mining Inf. Anal. Bull. 2022, no. 6−2, pp. 18—32. [In Russ]. DOI: 10.25018/0236_1493_2022_62_0_18.
20. Segui J. B., Higgins M. Blast design using measurement while drilling parameters. International Journal for Blasting and Fragmentation. 2002, vol. 6 (3–4), pp. 287–299.
21. Leighton J. C. Development of a Correlation between Rotary Drill Performance and Controlled Blasting Powder Factors, Master’s Thesis. Vancouver: University of British Columbia, Canada, 1982.
22. Zharikov S. N. On methods for studying soil properties to improve the efficiency of drilling and blasting operations. Bulletin of the Kuzbass State Technical University. 2016, no. 6(117), pp. 3—7. [In Russ].
23. Vinogradov Y. I., Khokhlov S. V., Zigangirov R. R., Miftakhov A. A., Suvorov Y. I. Optimization of specific energy consumption for rock crushing by explosion at deposits with complex geological structure. Journal of Mining Institute. 2024, vol. 266, pp. 231—245. [In Russ]. 
24. Ghosh R., Gustafson A., Schunnesson H. Development of a geological model for chargeability assessment of borehole using drill monitoring technique. International Journal of Rock Mechanics and Mining Sciences. 2018, vol. 109, pp. 9–18.
25. Navarro J., Segarra P., Sanchidrián J. A., Castedo R., Pérez Fortes A. P., Natale M., Lopez L. M. Application of an in-house MWD system for quarry blasting. Proceedings of the 12th International Symposium on Rock Fragmentation by Blasting, Fragblast 12, Luleå, Sweden, 11–13 June 2018, H. Schunnesson, D. Johansson (Eds), pp. 203−207.
26. Safiullin R. N., Safiullin R. R., Sorokin K. V., Kuzmin K. A., Rudko V. A. Integral Assessment of Influence Mechanism of Heavy Particle Generator on Hydrocarbon Composition of Vehicles Motor Fuel. International Journal of Engineering. 2024, vol. 37(8), pp. 1700–1706. DOI: 10.5829/ije.2024.37.08b.20.
27. Pospehov G. B., Norova L. P., Izotova V. A. Comparing the methods of grain size analysis of gypsum-containing sulfuric acid wastes neutralized with limestone. Sustainable Development of Mountain Territories. 2024, vol. 16, no. 4, pp. 1729–1742. [In Russ]. DOI: https://doi.org/10.21177/1998-4502-2024-16−4-1729−1742.
28. Saadoun A., Fredj M., Boukarm R., Hadji R. Fragmentation analysis using digital image processing and empirical model (KuzRam): a comparative study. Journal of Mining Institute. 2022, vol. 257, pp. 822–832. [In Russ]. DOI: 10.31897/PMI.2022.84. 
29. Ligotsky D. N., Dolgushin N. A. Analysis of experience in the use of unmanned technologies in open pit mining and prospects for their development. Mining Journal. 2025, no. 2, pp. 42–47. [In Russ]. DOI: 10.17580/gzh.2025.02.06.
30. Shabarov A. N., Noskov V. A., Pavlovich A. A., Cherepov A. A. Concept of geomechanical risk in open pit mining. Mining Journal. 2022, no. 9, pp. 22–28. [In Russ]. DOI: 10.17580/gzh.2022.09.04.
31. Kovalsky E., Kongar-Syuryun C., Morgoeva A., Klyuev R., Khayrutdinov M. Backfill for Advanced Potash Ore Mining Technologies. Technologies. 2025, vol. 13, 60. https://doi.org/10.3390/technologies13020060. 
32. Isheyskiy V. A., Martinyskin E. A., Vasilyev A. S., Smirnov S. A. Selection of data on drilling-and-blasting in creation of databases of machine learning algorithms. MIAB. Mining Inf. Anal. Bull. 2022, no. 4, pp. 116–133. [In Russ]. DOI: 10.25018/0236_1493_2022_4_0_116.
33. Fadeev A. A., Zaborskiy E. N., Bagdasaryan O. E. Digital solutions for drilling and blasting operations as a way to improve efficiency, safety of operations and reduce seismic impact during rock crushing. Ugol’. 2024, no. 12, pp. 99–102. [In Russ]. DOI: 10.18796/0041-5790-2024-12−99−102. 
34. Petyers K. I., Rada A. O., Konkov N. Yu. O podkhodakh k avtomatizirovannoy obrabotke dannykh pri analize telemetrii bureniya vzryvnykh skvazhin dlya razrabotki spetsializirovannogo programmnogo obespecheniya. Vzryvnoe delo. 2024, no. 144/101, pp. 52–72. 
35. Gusev V. N., Blishchenko A. A., Sannikova A. P. Study of a set of factors influencing the error of surveying mine facilities using a geodesic quadcopter . Journal of Mining Institute. 2022, vol. 254, pp. 173–179. DOI: 10.31897/PMI.2022.35. 

 

 

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