Deep learning-based rock type identification using drill vibration frequency spectrum images

References

• R. Ghosh, Assessment of Rock Mass Quality and Its Effects on Charge Ability Using Drill Monitoring Technique, Luleå University of Technology, Luleå, 2017.
   Google Scholar
• D.V. Ellis, Well Logging for Earth Scientists, 2nd ed. Springer, Dordrecht, The Netherlands, 2007.
   Google Scholar
• V. Isheyskiy and J.A. Sanchidrián, Prospects of applying MWD technology for quality management of drilling and blasting operations at mining enterprises, Minerals 10 (10) (2020), pp. 925. doi:10.3390/min10100925.
   Web of Science ®Google Scholar
• Z. Li, K. Itakura, and Y. Ma, Survey of measurement-while-drilling technology for small-diameter drilling machines, Electron. J. Geotechnical. Eng. 19 (n.d), pp. 17.
   Google Scholar
• V. Vezhapparambu, J. Eidsvik, and S. Ellefmo, Rock classification using multivariate analysis of measurement while drilling data: Towards a better sampling strategy, Minerals 8 (9) (2018), pp. 384. doi:10.3390/min8090384.
   Web of Science ®Google Scholar
• R. Leung and S. Scheding, Automated coal seam detection using a modulated specific energy measure in a monitor-while-drilling context, Int. J. Rock Mech. Min. Sci. 75 (2015), pp. 196–209. doi:10.1016/j.ijrmms.2014.10.012.
   Web of Science ®Google Scholar
• S. Manzoor, Rock mass characterization using MWD data and photogrammetry, in Mining Goes Digital C. Mueller, ed. 1st ed. CRC Press, London, 2019, pp. 217–225. doi: 10.1201/9780429320774-25.
   Google Scholar
• M. Zborovjan and I. Leššo, Acoustic identification of rocks during drilling. Acta Montan. Slovaca. 8 (2003), pp. 3.
   Google Scholar
• S. Shreedharan, C. Hegde, S. Sharma, and H. Vardhan, Acoustic fingerprinting for rock identification during drilling, IJMME 5 (2) (2014), pp. 89. doi:10.1504/IJMME.2014.060193.
   Google Scholar
• M. Khoshouei and R. Bagherpour, Predicting the geomechanical properties of hard rocks using analysis of the acoustic and vibration signals during the drilling operation, Geotech. Geol. Eng. 39 (3) (2021), pp. 2087–2099. doi:10.1007/s10706-020-01611-z.
   Web of Science ®Google Scholar
• B.B. Sinaice, Y. Kawamura, J. Kim, N. Okada, I. Kitahara, and H. Jang, Application of deep learning approaches in igneous rock hyperspectral imaging. In: E. Topal, ed. Proceedings of the 28th International Symposium on Mine Planning and Equipment Selection - MPES 2019, Springer Series in Geomechanics and Geoengineering, Springer International Publishing, Cham, 2020, pp. 228–235. doi:10.1007/978-3-030-33954-8_29.
   Google Scholar
• Z. Xu, W. Ma, P. Lin, H. Shi, D. Pan, and T. Liu, Deep learning of rock images for intelligent lithology identification, Comput. Geosci. 154 (2021), pp. 104799. doi:10.1016/j.cageo.2021.104799.
   Web of Science ®Google Scholar
• dos Anjos, CEM, M.R.V. Avila, A.G.P. Vasconcelos, A.M. Pereira Neta, L.C. Medeiros, A.G. Evsukoff, R. Surmas, and L. Landau, Deep learning for lithological classification of carbonate rock micro-CT images, Comput. Geosci. 25 (3) (2021), pp. 971–983. doi:10.1007/s10596-021-10033-6.
   Web of Science ®Google Scholar
• D. Li, R. Shirani Faradonbeh, A. Lv, X. Wang, and H. Roshan, A data-driven field-scale approach to estimate the permeability of fractured rocks, Int. J. Min. Reclam. Environ. 36 (10) (2022), pp. 671–687. doi:10.1080/17480930.2022.2086769.
   Web of Science ®Google Scholar
• B. Yu, D. Zhang, B. Xu, Y. Liu, H. Zhao, and C. Wang, Modeling of true triaxial strength of rocks based on optimized genetic programming, Appl. Soft Comput. 129 (2022), pp. 109601. Available at. 10.1016/j.asoc.2022.109601.
   Web of Science ®Google Scholar
• C. Kumar, H. Vardhan, C. Murthy, and N.C. Karmakar, Estimating rock properties using sound signal dominant frequencies during diamond core drilling operations, J. Rock Mech. Geotech. Eng. 11 (4) (2019), pp. 850–859. doi:10.1016/j.jrmge.2019.01.001.
   Web of Science ®Google Scholar
• Y. Xie, C. Zhu, W. Zhou, Z. Li, X. Liu, and M. Tu, Evaluation of machine learning methods for formation lithology identification: A comparison of tuning processes and model performances, J. Pet. Sci. Eng. 160 (2018), pp. 182–193. doi:10.1016/j.petrol.2017.10.028.
   Web of Science ®Google Scholar
• Y. Imamverdiyev and L. Sukhostat, Lithological facies classification using deep convolutional neural network, J. Pet. Sci. Eng. 174 (2019), pp. 216–228. doi:10.1016/j.petrol.2018.11.023.
   Web of Science ®Google Scholar
• X. Hu, L. Chu, J. Pei, W. Liu, and J. Bian, Model complexity of deep learning: A survey, Knowl. Inf. Syst. 63 (10) (2021), pp. 2585–2619. doi:10.1007/s10115-021-01605-0.
   Web of Science ®Google Scholar
• L. Senjoba, Y. Kosugi, M. Hisada, and Y. Kawamura, Lithology identification during rotary percussion drilling based on acceleration waveform 1D convolutional neural network, in Rock Mechanics and Engineering Geology in Volcanic Fields, CRC Press, London, 2022, pp. 435–441. doi:10.1201/9781003293590-54.
   Google Scholar
• A. Lesne, The discrete versus continuous controversy in physics, Math. Struct. Comp. Sci. 17 (2) (2007), pp. 185–223. doi:10.1017/S0960129507005944.
   Web of Science ®Google Scholar
• M. Khoshouei, R. Bagherpour, and M.H. Jalalian, Rock type identification using analysis of the acoustic signal frequency contents propagated while drilling operation, Geotech. Geol. Eng. 40 (3) (2022), pp. 1237–1250. doi:10.1007/s10706-021-01957-y.
   Web of Science ®Google Scholar
• D. Peng, Z. Liu, H. Wang, Y. Qin, and L. Jia, A novel deeper one-dimensional CNN with residual learning for fault diagnosis of wheelset bearings in high-speed trains, IEEE. Access 7 (2019), pp. 10278–10293. doi:10.1109/ACCESS.2018.2888842.
   Web of Science ®Google Scholar
• J. Grezmak, P. Wang, C. Sun, and R.X. Gao, Explainable convolutional neural network for gearbox fault diagnosis, Procedia. CIRP 80 (2019), pp. 476–481. doi:10.1016/j.procir.2018.12.008.
   Google Scholar
• B. Zhao, H. Lu, S. Chen, J. Liu, and D. Wu, Convolutional neural networks for time series classification, J. Syst. Eng. Electron. 28 (1) (2017), pp. 162–169. doi:10.21629/JSEE.2017.01.18.
   Web of Science ®Google Scholar
• D. Cian, J. van Gemert, and A. Lengyel, Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task, arXiv: 2008.01584 [cs]. (2020), [Preprint]. http://arxiv.org/abs/2008.01584.
   Google Scholar
• R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, Grad-CAM: Visual explanations from deep networks via gradient-based localization, Int. J. Comput. Vis. 128 (2020), pp. 336–359. doi:10.1007/s11263-019-01228-7.
   Web of Science ®Google Scholar
• F. Chen and J.Y. Tsou, Assessing the effects of convolutional neural network architectural factors on model performance for remote sensing image classification: An in-depth investigation, Int. J. Appl. Earth Obs. Geoinf. 112 (2022), pp. 102865. doi:10.1016/j.jag.2022.102865.
   Web of Science ®Google Scholar
• K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770–778.
   Google Scholar
• C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 2818–2826.
   Google Scholar
• G. Huang, Z. Liu, L. van der Maaten, and K.Q. Weinberger, Densely connected convolutional networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 4700–4708.
   Google Scholar
• G. Chen, M. Chen, G. Hong, Y. Lu, B. Zhou, and Y. Gao, A new method of lithology classification based on convolutional neural network algorithm by utilizing drilling string vibration data, Energies 13 (4) (2020), pp. 888. doi:10.3390/en13040888.
   Web of Science ®Google Scholar
• Y. Yoo and S. Jeong, Vibration analysis process based on spectrogram using gradient class activation map with selection process of CNN model and feature layer, Displays 73 (2022), pp. 102233. doi:10.1016/j.displa.2022.102233.
   Web of Science ®Google Scholar
• M. Qin, K. Wang, K. Pan, T. Sun, and Z. Liu, Analysis of signal characteristics from rock drilling based on vibration and acoustic sensor approaches, Appl. Acoust. 140 (2018), pp. 275–282. doi:10.1016/j.apacoust.2018.06.003.
   Web of Science ®Google Scholar