Geomechanics and Engineering

  • 제36권3호
  • /
  • Pages.205-215
  • /
  • 2024
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Automatic detection of discontinuity trace maps: A study of image processing techniques in building stone mines

  • Mojtaba Taghizadeh (Department of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology) ;
  • Reza Khalou Kakaee (Department of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology) ;
  • Hossein Mirzaee Nasirabad (Department of Mining Engineering, Sahand University of Technology) ;
  • Farhan A. Alenizi (Department of Electrical Engineering, College of engineering, Prince Sattam Bin Abdulaziz University)
  • 투고 : 2023.09.03
  • 심사 : 2023.11.21
  • 발행 : 2024.02.10
⟨ 이전 논문 다음 논문 ⟩

초록

Manually mapping fractures in construction stone mines is challenging, time-consuming, and hazardous. In this method, there is no physical access to all points. In contrast, digital image processing offers a safe, cost-effective, and fast alternative, with the capability to map all joints. In this study, two methods of detecting the trace of discontinuities using image processing in construction stone mines are presented. To achieve this, we employ two modified Hough transform algorithms and the degree of neighborhood technique. Initially, we introduced a method for selecting the best edge detector and smoothing algorithms. Subsequently, the Canny detector and median smoother were identified as the most efficient tools. To trace discontinuities using the mentioned methods, common preprocessing steps were initially applied to the image. Following this, each of the two algorithms followed a distinct approach. The Hough transform algorithm was first applied to the image, and the traces were represented through line drawings. Subsequently, the Hough transform results were refined using fuzzy clustering and reduced clustering algorithms, along with a novel algorithm known as the farthest points' algorithm. Additionally, we developed another algorithm, the degree of neighborhood, tailored for detecting discontinuity traces in construction stones. After completing the common preprocessing steps, the thinning operation was performed on the target image, and the degree of neighborhood for lineament pixels was determined. Subsequently, short lines were removed, and the discontinuities were determined based on the degree of neighborhood. In the final step, we connected lines that were previously separated using the method to be described. The comparison of results demonstrates that image processing is a suitable tool for identifying rock mass discontinuity traces. Finally, a comparison of two images from different construction stone mines presented at the end of this study reveals that in images with fewer traces of discontinuities and a softer texture, both algorithms effectively detect the discontinuity traces.

키워드

과제정보

This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2024/R/1445).

참고문헌

  1. Berisavljevic, Z., Berisavljevic, D., Marjanovic, M. and Melentijevic, S. (2023), "Probabilistic analysis of anisotropic rock slope with reinforcement measures", Geomech. Eng., 34(3), 285-301. https://doi.org/10.12989/gae.2023.34.3.285. 
  2. Burger, W. and Burge, M.J. (2009), Principles of Digital Image Processing, 111, London: Springer. https://doi.org/10.1007/978-1-84882-919-0. 
  3. Cardu, M., Godio, A., Oggeri, C. and Seccatore, J. (2022), "The influence of rock mass fracturing on splitting and contour blasts", Geomech. Geoeng., 17(3), 822-833. https://doi.org/10.1080/17486025.2021.1890234. 
  4. Chen, Y., Xu, J., Peng, S., Zhang, Q. and Chen, C. (2022), "Strain localization and seepage characteristics of rock under triaxial compression by 3D digital image correlation", Int. J. Rock Mech. Min. Sci., 152, 105064. https://doi.org/10.1016/j.ijrmms.2022.105064. 
  5. Chen, J., Wen, L., Bi, C., Liu, Z., Liu, X., Yin, L. and Zheng, W. (2023), "Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt", Open Geosci., 15(1). https://doi.org/10.1515/geo-2022-0482. 
  6. Cheng, Y., Lan, S., Fan, X., Tjahjadi, T., Jin, S. and Cao, L. (2023), "A dual-branch weakly supervised learning based network for accurate mapping of woody vegetation from remote sensing images", Int. J. Appl. Earth Observ. Geoinform., 124, 103499. https://doi.org/10.1016/j.jag.2023.103499. 
  7. Dong, W., Zhao, J., Qu, J., Xiao, S., Li, N., Hou, S. and Li, Y. (2023a), "Abundance matrix correlation analysis network based on hierarchical multihead self-cross-hybrid attention for hyperspectral change detection", IEEE Trans. Geosci. Remote Sens., 61, 1-13. https://doi.org/10.1109/TGRS.2023.3235401. 
  8. Dong, W., Yang, Y., Qu, J., Xiao, S. and Li, Y. (2023b), "Local information-enhanced graph-transformer for hyperspectral image change detection with limited training samples", IEEE T. Geosci. Remote Sens., 61, 1-14. https://doi.org/10.1109/TGRS.2023.3269892. 
  9. Deb, D., Hariharan, S., Rao, U.M. and Ryu, C.H. (2008), "Automatic detection and analysis of discontinuity geometry of rock mass from digital images", Comput. Geosci., 34(2), 115-126. https://doi.org/10.1016/j.cageo.2007.03.007. 
  10. Dougherty, G. (2009), Digital Image Processing for Medical Applications. Cambridge University Press. 
  11. Ghabraie, B., Ren, G., Smith, J. and Holden, L. (2015), "Application of 3D laser scanner, optical transducers and digital image processing techniques in physical modelling of miningrelated strata movement", Int. J. Rock Mech. Min. Sci., 80, 219-230. https://doi.org/10.1016/j.ijrmms.2015.09.025. 
  12. Gonzales, R.C., Woods, R.E. and Eddins, S.L. (2004), Digital Image Processing using MATLAB. Pearson Prentice Hall. 
  13. Gonzalez, R.C. and Woods, R.E. (2002), Digital Image Processing. upper saddle River. J.: Prentice Hall. 
  14. Hadjigeorgiou, J., Lemy, F., Cote, P. and Maldague, X. (2003), "An evaluation of image analysis algorithms for constructing discontinuity trace maps". https://repository.geologyscience.ru/bitstream/handle/123456789/35239/Hadj_03.pdf?sequence=1. 
  15. Kemeny, J., Norton, B. and Turner, K. (2006), "Rock slope stability analysis utilizing ground-based LiDAR and digital image processing", Felsbau, 24(3), 8-15. https://www.researchgate.net/publication/286549851_Rock_slope_stability_analysis_utilizing_groundbased_LIDAR_and_digit al_image_processing. 
  16. Khadivi, B., Heidarpour, A., Zhang, Q. and Masoumi, H. (2023), "Characterizing the cracking process of various rock types under Brazilian loading based on coupled Acoustic Emission and high-speed imaging techniques", Int. J. Rock Mech Min. Sci., 168, 105417. https://doi.org/10.1016/j.ijrmms.2023.105417. 
  17. Lee, Y.K., Kim, J., Choi, C.S. and Song, J.J. (2022), "Semiautomatic calculation of joint trace length from digital images based on deep learning and data structuring techniques", Int. J. Rock Mech. Min. Sci., 149, 104981. https://doi.org/10.1016/j.ijrmms.2021.104981. 
  18. Li, R., Lu, W., Chen, M., Wang, G., Xia, W. and Yan, P. (2021), "Quantitative analysis of shapes and specific surface area of blasted fragments using image analysis and three-dimensional laser scanning", Int. J. Rock Mech. Min. Sci., 141, 104710. https://doi.org/10.1016/j.ijrmms.2021.104710. 
  19. Liu, C. and Liu, G. (2021), "Characterization of pore structure parameters of foam concrete by 3D reconstruction and image analysis", Constr. Build. Mater., 267, 120958. https://doi.org/10.1016/j.conbuildmat.2020.120958. 
  20. Li, R., Zhang, H., Chen, Z., Yu, N., Kong, W., Li, T., Wang, E., Wu, X. and Liu, Y. (2022a), "Denoising method of groundpenetrating radar signal based on independent component analysis with multifractal spectrum", Measurement, 192, 110886. https://doi.org/10.1016/j.measurement.2022.110886. 
  21. Li, J., Wang, Y., Nguyen, X., Zhuang, X., Li, J., Querol, X., Li, B., Moreno, N., Hoang, V., Cordoba, P. and Do, V. (2022b), "First insights into mineralogy, geochemistry, and isotopic signatures of the Upper Triassic high‑sulfur coals from the Thai Nguyen Coal field, NE Vietnam", Int. J. Coal Geol., 261, 104097. https://doi.org/10.1016/j.coal.2022.104097. 
  22. Li, Q., Lu, L., Zhao, Q. and Hu, S. (2022c), "Impact of inorganic solutes' release in groundwater during oil shale in situ exploitation", Water, 15(1), 172. https://doi.org/10.3390/w15010172. 
  23. Li, J., Lin, Y., Nguyen, X., Zhuang, X., Li, B., Querol, X., Moreno, N. and Cordoba, P. (2023), "Enrichment of strategic metals in the Upper Triassic coal from the Nui Hong open-pit mine, Thai Nguyen Coalfield, NE Vietnam", Ore Geology Reviews, 153, 105301. https://doi.org/10.1016/j.oregeorev.2023.105301. 
  24. Liu, Q., Yuan, H., Hamzaoui, R., Su, H., Hou, J. and Yang, H. (2021), "Reduced reference perceptual quality model with application to rate control for video-based point cloud compression", IEEE T. Image Process., 30, 6623-6636. https://doi.org/10.1109/TIP.2021.3096060. 
  25. Liu, W., Zhou, H., Zhang, S. and Zhao, C. (2023), "Variable parameter creep model based on the separation of viscoelastic and viscoplastic deformations", Rock Mech. Rock Eng., 56(6), 4629-4645. https://doi.org/10.1007/s00603-023-03266-7. 
  26. Ma, S., Qiu, H., Yang, D., Wang, J., Zhu, Y., Tang, B., Sun, K. and Cao, M. (2023), "Surface multi-hazard effect of underground coal mining", Landslides, 20(1), 39-52. https://doi.org/10.1007/s10346-022-01961-0. 
  27. Ma, K., Huang, X., Shen, J., Hu, M., Long, G., Xie, Y. and Zhang, W. (2021), "The morphological characteristics of brick-concrete recycled coarse aggregate based on the digital image processing technique", J. Build. Eng., 44, 103292. https://doi.org/10.1016/j.jobe.2021.103292. 
  28. Moomivand, H., Seadati, S. and Allahverdizadeh, H. (2021), "A new approach to improve the assessment of rock mass discontinuity spacing using image analysis technique", Int. J. Rock Mech. Min. Sci., 143, 104760. https://doi.org/10.1016/j.ijrmms.2021.104760. 
  29. Noori, O. and Panda, S.S. (2016). "Site-specific management of common olive: Remote sensing, geospatial, and advanced image processing applications", Comput. Electron. Agr., 127, 680-689. https://doi.org/10.1016/j.compag.2016.07.031. 
  30. Priest, S.D. (1993), Discontinuity Analysis for Rock Engineering. Springer Science & Business Media. https://doi.org/10.1007/978-94-011-1498-1. 
  31. Ren, C., Yu, J., Liu, S., Yao, W., Zhu, Y. and Liu, X. (2022), "A plastic strain-induced damage model of porous rock suitable for different stress paths", Rock Mech. Rock Eng., 55(4), 1887-1906. https://doi.org/10.1007/s00603-022-02775-1. 
  32. Ren, C., Yu, J., Zhang, C., Liu, X., Zhu, Y. and Yao, W. (2023), "Micro-macro approach of anisotropic damage: A semianalytical constitutive model of porous cracked rock", Eng. Fract. Mech., 290, 109483. https://doi.org/10.1016/j.engfracmech.2023.109483. 
  33. Shi, Y., Xi, J., Hu, D., Cai, Z. and Xu, K. (2023), "RayMVSNet++: learning ray-based 1D implicit fields for accurate multi-view stereo", IEEE T. Pattern Anal. Machine Intell., 1-17. https://doi.org/10.1109/TPAMI.2023.3296163. 
  34. Saricam, T. and Ozturk, H. (2018), "Estimation of RQD by digital image analysis using a shadow-based method", Int. J. Rock Mech. Min. Sci., 112, 253-265. https://doi.org/10.1016/j.ijrmms.2018.10.032. 
  35. Tao, Y., Shi, J., Guo, W. and Zheng, J. (2023), "Convolutional neural network based defect recognition model for phased array ultrasonic testing images of electrofusion joints", J. Pressure Vessel T. - ASME, 145(2). https://doi.org/10.1115/1.4056836. 
  36. Tie, Y., Rui, X., Shi-Hui, S., Zhao-Kai, H. and Jin-Yu, F. (2023), "A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm", Petroleum Sci., https://doi.org/10.1016/j.petsci.2023.09.011 
  37. Tian, W.L., Yang, S.Q., Dong, J.P., Cheng, J.L. and Lu, J.W. (2022), "An experimental study on triaxial failure mechanical behavior of jointed specimens with different JRC", Geomech. Eng., 28(2), 181-195. https://doi.org/10.12989/gae.2022.28.2.181. 
  38. Vaziri, M.R., Tavakoli, H. and Bahaaddini, M. (2022), "2D numerical study of the mechanical behaviour of non-persistent jointed rock masses under uniaxial and biaxial compression tests", Geomech. Eng., 28(2), 117-133. https://doi.org/10.12989/gae.2022.28.2.117 
  39. Wu, M., Ba, Z and Liang, J. (2022), "A procedure for 3D simulation of seismic wave propagation considering source-path-site effects: Theory, verification and application", Earthq. Eng. Struct. D., 51(12), 2925-2955. https://doi.org/10.1002/eqe.3708. 
  40. Wang, J., Guo, L., Bai, Z. and Yang, L. (2016). "Using computed tomography (CT) images and multi-fractal theory to quantify the pore distribution of reconstructed soils during ecological restoration in opencast coal-mine", Ecol. Eng., 92, 148-157. https://doi.org/10.1016/j.ecoleng.2016.03.029. 
  41. Wu, Q., Kulatilake, P.H.S.W. and Tang, H.M. (2011), "Comparison of rock discontinuity mean trace length and density estimation methods using discontinuity data from an outcrop in Wenchuan area, China", Comput. Geotech., 38(2), 258-268. https://doi.org/10.1016/j.compgeo.2010.12.003. 
  42. Xu, L., Cai, M., Dong, S., Yin, S., Xiao, T., Dai, Z., Wang, Y. and Reza Soltanian, M. (2022), "An upscaling approach to predict mine water inflow from roof sandstone aquifers", J. Hydrology, 612, 128314. https://doi.org/10.1016/j.jhydrol.2022.128314. 
  43. Yuan, Y., Zhang, N., Han, C., Yang, S., Xie, Z. and Wang, J. (2022), "Digital image processing-based automatic detection algorithm of cross joint trace and its application in mining roadway excavation practice", Int. J. Min. Sci. Tech., 32(6), 1219-1231. https://doi.org/10.1016/j.ijmst.2022.09.009. 
  44. Yang, M., Wang, H., Hu, K., Yin, G. and Wei, Z. (2022), "IANet$:$ an inception-attention-module-based network for classifying underwater images from others", IEEE J. Oceanic Eng., 47(3), 704-717. https://doi.org/10.1109/JOE.2021.3126090. 
  45. Yao, W., Yu, J., Liu, X., Zhang, Z., Feng, X. and Cai, Y. (2023), "Experimental and theoretical investigation of coupled damage of rock under combined disturbance", Int. J. Rock Mech. Min. Sci., 164, 105355. https://doi.org/10.1016/j.ijrmms.2023.105355. 
  46. Yu, J., Zhu, Y., Yao, W., Liu, X., Ren, C., Cai, Y. and Tang, X. (2021). "Stress relaxation behaviour of marble under cyclic weak disturbance and confining pressures", Measurement, 182, 109777. https://doi.org/10.1016/j.measurement.2021.109777. 
  47. Zhuo, Z., Du, L., Lu, X., Chen, J. and Cao, Z. (2022a), "Smoothed Lv distribution based three-dimensional imaging for spinning space debris", IEEE T. Geosci. Remote Sens., 60, 1-13. https://doi.org/10.1109/TGRS.2022.3174677. 
  48. Zhou, G., Li, H., Song, R., Wang, Q., Xu, J. and Song, B. (2022b), "Orthorectification of fisheye image under equidistant projection model", Remote Sens., 14(17), 4175. https://doi.org/10.3390/rs14174175. 
  49. Zhou, G., Liu, W., Zhu, Q., Lu, Y. and Liu, Y. (2022c). "ECAMobileNetV3(Large)+SegNet model for binary sugarcane classification of remotely sensed images", IEEE T. Geosci. Remote Sens., 60, 1-15. https://doi.org/10.1109/TGRS.2022.3215802. 
  50. Zhou, G., Wang, Q., Huang, Y., Tian, J., Li, H. and Wang, Y. (2022d), "True2 Orthoimage Map Generation", Remote Sens., 14(17), 4396. https://doi.org/10.3390/rs14174396 
  51. Zhang, B., Li, Y., Yang, X.Y., Li, S.C., Wei, C. and Songa, J. (2023), "Influence of size and location of a pre-existing fracture on hydraulic fracture propagation path", Geomech. Eng., 32(3), 321. https://doi.org/10.12989/gae.2023.32.3.321.