Multiscale method for identifying and marking the multiform fractures from visible-light rock-mass images

Abstract

Multiform fractures have a direct impact on the mechanical performance of rock masses. To accurately identify multiform fractures, the distribution patterns of grayscale and the differential features of fractures in their neighborhoods are summarized. Based on this, a multiscale processing algorithm is proposed. The multiscale process is as follows. On the neighborhood of pixels, a grayscale continuous function is constructed using bilinear interpolation, the smoothing of the grayscale function is realized by Gaussian local filtering, and the grayscale gradient and Hessian matrix are calculated with high accuracy. On small-scale blocks, the pixels are classified by adaptively setting the grayscale threshold to identify potential line segments and mini-fillings. On the global image, potential line segments and mini-fillings are spliced together by progressing the block frontier layer-by-layer to identify and mark multiform fractures. The accuracy of identifying multiform fractures is improved by constructing a grayscale continuous function and adaptively setting the grayscale thresholds on small-scale blocks. And the layer-by-layer splicing algorithm is performed only on the domain of the 2-layer small-scale blocks, reducing the complexity. By using rock mass images with different fracture types as examples, the identification results show that the proposed algorithm can accurately identify the multiform fractures, which lays the foundation for calculating the mechanical parameters of rock masses.

Keywords

Visible light rock-mass images / Continuous grayscale function / Small-scale blocks / Multiform fractures

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was financially supported by National Natural Science Foundation of China (Grant No. 51739007), and National Key Research and Development Program of China (Grant No. 2016YFB1100602).

References

[1] Azarafza, M., Ghazifard, A., Akgün, H., & Ebrahim, A. K. (2019). Development of a 2d and 3d computational algorithm for discontinuity structural geometry identification by artificial intelligence based on image processing techniques. Bulletin of Engineering Geology and the Environment, 78, 3371-3383.

[2] Azarafza, M., Nanehkaran, Y. A., Akgun, H., & Mao, Y. M. (2021). Application of an image processing-based algorithm for river-side granular sediment gradation distribution analysis. Advances in Materials Research, 10(3), 229-244.

[3] Buyer, A., Aichinger, S., & Schubert, W. (2020). Applying photogrammetry and semi-automated joint mapping for rock mass characterization. Engineering Geology, 264, 105332.

[4] Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679-698.

[5] Chen, J. Y., Chen, Y. F., Cohn, A. G., Huang, H. W., Man, J. H., & Wei, L. J. (2022). A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face. Journal of Rock Mechanics and Geotechnical Engineering, 14(4), 1077-1088.

[6] Chen, J. Y., Zhou, M. L., Huang, H. W., Zhang, D. M., & Peng, Z. C. (2021). Automated extraction and evaluation of fracture trace maps from rock tunnel face images via deep learning. International Journal of Rock Mechanics and Mining Sciences, 142, 104745.

[7] Cui, J.Z., Han, F., & Shan, Y.J. (2009). Statistical multi-scale method of mechanics parameter prediction for rock mass with random cracks/ joints distribution. In Computational Structural Engineering: Proceedings of the International Symposium on Computational Structural Engineering, pp. 49-62.

[8] Cui, J. Z., & Yu, Y. F. (2020). Elastic-plastic decompositipn method of metalllc structure based on molecule dynamics simulation. Mathematica Numerica Sinica, 42(3), 279.

[9] Deb, D., Hariharan, S., Rao, U. M., & Ryu, C. H. (2008). Automatic detection and analysis of discontinuity geometry of rock mass from digital images. Computers and Geosciences, 34(2), 115-126.

[10] Feng, Q. H., & Röshoff, K. (2015). A survey of 3d laser scanning techniques for application to rock mechanics and rock engineering. In The ISRM Suggested Methods for Rock Characterization, Testing and Monitoring, 2007-2014, 265-293.

[11] Ge, Y. F., Tang, H. M., Xia, D., Wang, L. Q., Zhao, B. B., Teaway, J. W., Chen, H. Z., & Zhou, T. (2018). Automated measurements of discontinuity geometric properties from a 3d-point cloud based on a modified region growing algorithm. Engineering Geology, 242, 44-54.

[12] Guo, J. T., Liu, Y. H., Wu, L. X., Liu, S. J., Yang, T. H., Zhu, W. C., & Zhang, Z. R. (2019). A geometry-and texture-based automatic discontinuity trace extraction method for rock mass point cloud. International Journal of Rock Mechanics and Mining Sciences, 124, 104132.

[13] Guo, L.Q., Liao, J.B., Zhong, F.P., Dong, K.Y., Liu, X., Tao, J., & Chen, J.J. (2012). A semi-automated methodology for rock mass discontinuity trace map construction. In 2012 International Conference on Image Analysis and Signal Processing, pp. 1-5.

[14] Hadjigeorgiou, J., Lemy, F., Cote, P., & Maldague, X. (2003). An evaluation of image analysis algorithms for constructing discontinuity trace maps. Rock Mechanics and Rock Engineering, 36(2), 163-179.

[15] Haralick, R. M. (1984). Digital step edges from zero crossing of second directional derivatives. IEEE Transactions on Pattern Analysis Machine Intelligence, 6(1), 58-68.

[16] Hong, K., Han, E., & Kang, K. S. (2017). Determination of geological strength index of jointed rock mass based on image processing. Journal of Rock Mechanics and Geotechnical Engineering, 9(4), 702-708.

[17] Hsiao, P.Y., Chen, C.H., Chou, S.S., Li, L.T., & Chen, S.J. (2006). A parameterizable digital-approximated 2d gaussian smoothing filter for edge detection in noisy image. In International Symposium on Circuits and Systems ( ISCAS 2006), pp. 4.

[18] Kanopoulos, N., Vasanthavada, N., & Baker, R. L. (1988). Design of an image edge detection filter using the sobel operator. IEEE Journal of Solid-state Circuits, 23(2), 358-367.

[19] Kemeny, J., & Post, R. (2003). Estimating three-dimensional rock discontinuity orientation from digital images of fracture traces. Computers and Geosciences, 29(1), 65-77.

[20] Lee, Y. K., Kim, J., Choi, C. S., & Song, J. J. (2022). Semi-automatic calculation of joint trace length from digital images based on deep learning and data structuring techniques. International Journal of Rock Mechanics and Mining Sciences, 149, 104981.

[21] Lei, M. F., Liu, L. H., Shi, C. H., Tan, Y., Li, Y. X., & Wang, W. D. (2021). A novel tunnel-lining crack recognition system based on digital image technology. Tunnelling and Underground Space Technology, 108, 103724.

[22] Lemy, F., & Hadjigeorgiou, J. (2003). Discontinuity trace map construction using photographs of rock exposures. International Journal of Rock Mechanics and Mining Sciences, 40(6), 903-917.

[23] Lemy, F., & Hadjigeorgiou, J. (2004). A digital face mapping case study in an underground hard rock mine. Canadian Geotechnical Journal, 41(6), 1011-1025.

[24] Leng, B., Yang, H., Hou, G. P., & Lyamin, A. (2021). Rock mass trace line identification incorporated with grouping algorithm at tunnel faces. Tunnelling and Underground Space Technology, 110, 103810.

[25] Leu, S. S., & Chang, S. L. (2005). Digital image processing based approach for tunnel excavation faces. Automation in Construction, 14 (6), 750-765.

[26] Li, Y. Y., & Cui, J. Z. (2005). The multi-scale computational method for the mechanics parameters of the materials with random distribution of multi-scale grains. Composites Science and Technology, 65(9), 1447-1458.

[27] Li, H. B., Li, X. W., Li, W. Z., Zhang, S. L., & Zhou, J. W. (2019). Quantitative assessment for the rockfall hazard in a post-earthquake high rock slope using terrestrial laser scanning. Engineering Geology, 248, 1-13.

[28] Li, Q. Q., Zou, Q., Zhang, D. Q., & Mao, Q. Z. (2011). Fosa: F* seedgrowing approach for crack-line detection from pavement images. Image and Vision Computing, 29(12), 861-872.

[29] Liang, Y. J. (2016). Rock fracture skeleton tracing by image processing and quantitative analysis by geometry features. Journal of Geophysics and Engineering, 13(3), 273-284.

[30] Liu, Y. F., Fan, J. S., Nie, J. G., Kong, S., & Qi, Y. (2021). Review and prospect of digital-image-based crack detection of structure surface. China Civel Engineering Journal, 54(6), 20.

[31] Medina, R., Llamas, J., Jaime, G. G. B., Zalama, E., & Segarra, M. J. (2017). Crack detection in concrete tunnels using a gabor filter invariant to rotation. Sensors, 17(7), 1670.

[32] Mukherjee, S., Condron, B., & Acton, S. T. (2014). Tubularity flow fielda technique for automatic neuron segmentation. IEEE Transactions on Image Processing, 24(1), 374-389.

[33] Pun, T. (1980). A new method for grey-level picture thresholding using the entropy of the histogram. Signal processing, 2(3), 223-237.

[34] Reid, T., & Harrison, J. (2000). A semi-automated methodology for discontinuity trace detection in digital images of rock mass exposures. International Journal of Rock Mechanics and Mining Sciences, 37(7), 1073-1089.

[35] Roberts, L. G. (1963). Machine perception of three-dimensional solids. Massachusetts Institute of Technology, PhD thesis.

[36] Scucka, J., Martinec, P., Snuparek, R., & Vesely, V. (2006). Image processing and analysis in geotechnical investigation. Tunnelling and Underground Space Technology, 21(3), 223-223.

[37] Slob, S., Van Knapen, B., Hack, R., Turner, K., & Kemeny, J. (2005). Method for automated discontinuity analysis of rock slopes with threedimensional laser scanning. Transportation Research Record, 1913(1), 187-194.

[38] Sunayama, T. S. T., Ikebe, M. I. M., Asai, T. A. T., & Amemiya, Y. A. Y. (2000). Cellular m mos circuits performing edge detection with difference-of-gaussian filters. Japanese Journal of Applied Physics, 39 (4), 2278.

[39] Talab, A. M. A., Huang, Z. C., Xi, F., & Liu, H. M. (2016). Detection crack in image using otsu method and multiple filtering in image processing techniques. Optik, 127(3), 1030-1033.

[40] Tan, X., Konietzky, H., & Chen, W. (2016). Numerical simulation of heterogeneous rock using discrete element model based on digital image processing. Rock Mechanics and Rock Engineering, 49(12), 4957-4964.

[41] Tang, Y. D., He, L., Lu, W., Huang, X., Wei, H., & Xiao, H. G. (2021a). A novel approach for fracture skeleton extraction from rock surface images. International Journal of Rock Mechanics and Mining Sciences, 142, 104732.

[42] Tang, Y. D., He, L., Xiao, H. G., Wang, R. H., Lu, W., & Xu, T. (2021b). Fracture extraction from smooth rock surfaces using depth image segmentation. Rock Mechanics and Rock Engineering, 54(8), 3873-3889.

[43] Umili, G., Ferrero, A., & Einstein, H. H. (2013). A new method for automatic discontinuity traces sampling on rock mass 3d model. Computers and Geosciences, 51, 182-192.

[44] Wang, W. X., & Liang, Y. J. (2015). Rock fracture centerline extraction based on hessian matrix and steger algorithm. KSII Transactions on Internet and Information Systems, 9(12), 5073-5086.

[45] Wang, X. (2007). Laplacian operator-based edge detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(5), 886-890.

[46] Yang, S., Liu, S. M., Zhang, N., Li, G. C., & Zhang, J. (2021). A fully automatic-image-based approach to quantifying the geological strength index of underground rock mass. International Journal of Rock Mechanics and Mining Sciences, 140, 104585.

[47] Yu, Y., Cui, J. Z., & Han, F. (2009). The statistical second-order two-scale analysis method for heat conduction performances of the composite structure with inconsistent random distribution. Computational Materials Science, 46(1), 151-161.

[48] Yuan, Y. X., Zhang, N., Han, C. L., Yang, S., Xie, Z. Z., & Wang, J. (2022). Digital image processing-based automatic detection algorithm of cross joint trace and its application in mining roadway excavation practice. International Journal of Mining Science and Technology, 32(6), 1219-1231.

[49] Yue, Z. Q., Chen, S., & Tham, L. G. (2003). Finite element modeling of geomaterials using digital image processing. Computers and Geotechnics, 30(5), 375-397.

[50] Zhang, K. S., Wu, W., Zhu, H. H., Zhang, L. Y., Li, X. J., & Zhang, H. (2020). A modified method of discontinuity trace mapping using threedimensional point clouds of rock mass surfaces. Journal of Rock Mechanics and Geotechnical Engineering, 12(3), 571-586.

[51] Zhang, P., Zhao, Q. Y., Tannant, D. D., Ji, T. T., & Zhu, H. H. (2019a). 3d mapping of discontinuity traces using fusion of point cloud and image data. Bulletin of Engineering Geology and the Environment, 78(4), 2789-2801.

[52] Zhang, Q., Wang, X. J., He, L., & Tian, L. G. (2021). Estimation of fracture orientation distributions from a sampling window based on geometric probabilistic method. Rock Mechanics and Rock Engineering, 54(6), 3051-3075.

[53] Zhang, Y. C., Yue, P., Zhang, G. K., Guan, T., Lv, M. M., & Zhong, D. H. (2019b). Augmented reality mapping of rock mass discontinuities and rockfall susceptibility based on unmanned aerial vehicle photogrammetry. Remote Sensing, 11(11), 1311.

[54] Zhang, Y. B., Zhao, T. B., Yin, Y. C., Tan, Y. L., & Qiu, Y. (2019c). Numerical research on energy evolution in granite under different confining pressures using otsus digital image processing and pfc2d. Symmetry, 11(2), 131.

[55] Zheng, T., Zhao, Q. H., Su, R., & Hu, J. B. (2022). A framework for detecting and extracting discontinuities based on machine learning. Mining, Metallurgy and Exploration, 1-16.