煤岩微观相态FCN智能识别与分形重构

doi:10.13722/j.cnki.jrme.2019.0931

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (1312 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Digital core establishment，as an ideal model to study the physical and mechanical properties of rock, provides an undifferentiated numerical simulation. However，high level of accurate and efficient modeling restricts the promotion of digital core reconstruction technology. The traditional methods are time-consuming and laborious in processing CT slice based scanning data，due to limited number of scanning layers and the pore-fracture recognition depending on the traditional threshold segmentation algorithm. Taking coal as an example，the artificial intelligence recognition is introduced to realize the recognition of four micro phase states of pore，fracture，high-density mineral and coal matrix，and the fractal reconstruction is carried out for filling in information gaps. Data sets of four micro phase states are established and enhanced based on micro CT scanning，and a labelling software is developed for effectively distinguishing four kinds of micro-phases of materials. Especially for improving the efficiency and precision of identification，the FCN architecture is optimized and the Crack-FCN network structure is proposed，which has few network layers and low error rate. Moreover，the Potrace algorithm is introduced to quantitatively calculate fracture area，length and width，and the centerline extraction algorithm is introduced to effectively determine the complex topology. Considering the fractal similarity of fractured surface and to solve the problem of missing information between two adjacent CT slices，a fractal reconstruction algorithm is developed dependent on the local self-similar property and then optimized to improve the computational efficiency. Compared to the line interpolation and cubic spline interpolation，the fractal interpolation is more effective to describe the local roughness，and more importantly，the accuracy of intelligent recognition will continue to be improved with the continuous enhancement of data-set. This paper breaks through the traditional view and introduces the FCN into construct digital core of rock，and provides new technical support for the efficient and accurate establishment of numerical modelling.

Key words： rock mechanics digital core CT slices artificial intelligence identification fully convolutional neural network microphase of material fractal reconstruction

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	XUE Dongjie1
	2
	3
	TANG Qichun1
	WANG Ao4
	YI Haiyang5
	ZHANG Chi6
	GENG Chuanqing1
	ZHOU Hongwei2
	4

Cite this article:

XUE Dongjie1,2,3, et al. FCN-based intelligent identification and fractal reconstruction of pore-fracture network in coal by micro CT scanning#br#[J]. , 2020, 39(6): 1203-1221.

URL:

https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2019.0931 OR https://rockmech.whrsm.ac.cn/EN/Y2020/V39/I6/1203

[1] JACKSON P C. Introduction to artificial intelligence[M]. [S.l.]：Courier Dover Publications，2019：330–385.
[2] ANGNOSTOPOULOS C N E，ANGNOSTOPOULOS I E，LOUMOS V，et al. A license plate-recognition algorithm for intelligent transportation system applications[J]. IEEE Transactions on Intelligent Transportation Systems，2006，7(3)：377–392.
[3] 薛东杰，唐麒淳，王傲，等. 基于FCN的岩石混凝土裂隙几何智能识别[J]. 岩石力学与工程学报，2019，38(增2)：2 861–2 871. (XUE Dongjie，TANG Qichun，WANG Ao，et al. FCN-based intelligent identification of crack geometry in rock or concrete[J]. Chinese Journal of Rock Mechanics and Engineering，2019，38(Supp.2)：2 861–2 871.(in Chinese))
[4] 黄宏伟，李庆桐. 基于深度学习的盾构隧道渗漏水病害图像识别[J]. 岩石力学与工程学报，2017，36(12)：2 861–2 871.(HUANG Hongwei，LI Qingtong. Image recognition for water leakage in shield tunnel based on deep learning[J]. Chinese Journal of Rock Mechanics and Engineering，2017，36(12)：2 861–2 871.(in Chinese))
[5] XUE D，ZHOU H，ZHAO Y，et al. Real-time SEM observation of mesoscale failures under thermal-mechanical coupling sequences in granite[J]. International Journal of Rock Mechanics and Mining Sciences，2018，112：35–46.
[6] ZHOU H W，ZHONG J C，REN W G，et al. Characterization of pore-fracture networks and their evolution at various measurement scales in coal samples using X-ray μCT and a fractal method[J]. International Journal of Coal Geology，2018，189：35–49.
[7] JU Y，ZHENG J，EPSTEIN M，et al. 3D numerical reconstruction of well-connected porous structure of rock using fractal algorithms[J]. Computer Methods in Applied Mechanics and Engineering，2014，279：212–226.
[8] YOUSSEF S，ROSNBERG E，GLAND N，et al. Quantitative 3D characterisation of the pore space of real rocks：improved µ-CT resolution and pore extraction methodology[C]// International Symposium of the Society of Core Analysts. Calgary：[s. n.]，2007：1–17.
[9] ZHOU T，ZHU J B. Application of 3D printing and micro-CT scan to rock dynamics[C]// Rock Dynamics：From Research to Engineering，Taylor & Francis Group，London，2016：247–252.
[10] MAYO S，JOSH M，NESTERETS Y，et al. Quantitative micro-porosity characterization using synchrotron micro-CT and xenon K-edge subtraction in sandstones，carbonates，shales and coal[J]. Fuel，2015，154：167–173.
[11] SRISUTTHIYAKORN N. Deep-learning methods for predicting permeability from 2D/3D binary-segmented images[C]// In SEG Technical Program Expanded Abstracts 2016. Society of Exploration Geophysicists，2016：3 042–3 046.
[12] CHENG G，GUO W. Rock images classification by using deep convolution neural network[C]// In Journal of Physics：Conference Series. [S.l.]：IOP Publishing，2017，887(1)：012089.
[13] FERREIRA A，GIRALDI G. Convolutional neural network approaches to granite tiles classification[J]. Expert Systems with Applications，2017，84：1–11.
[14] ZHANG P Y，SUN J M，JIANG Y J，et al. Deep learning method for lithology identification from borehole images[C]// In 79th EAGE Conference and Exhibition. [S. l.]：[s. n.]，2017：1–5.
[15] MOSSER L，DUBRULE O，BLUNT M J. Reconstruction of three-dimensional porous media using generative adversarial neural networks[J]. Physical Review E，2017，96(4)：043309.
[16] LALOY E，HÉRAULT R，LEE J，et al. Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network[J]. Advances in Water Resources，2017，110：387–405.
[17] ALQAHTANI N，ARMSTRONG R T，MOSTAGHIMI P. Deep learning convolutional neural networks to predict porous media properties[C]// In SPE Asia Pacific Oil and Gas Conference and Exhibition. [S. l.]：Society of Petroleum Engineers，2018：1–10.
[18] MARESCHAL J C. Fractal reconstruction of sea-floor topography[C]// Fractals in Geophysics. Birkhäuser，Basel：[s. n.]，1989：197–210.
[19] HINTON G E，SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science，2006，313(5786)：504–507.
[20] LECUN Y，BENGIO Y，HINTON G. Deep learning[J]. Nature，2015，521(7553)：436.
[21] GOODFELLOW I，BENGIO Y，COURVILLE A. Deep learning[M]. [S. l.]：MIT Press，2016：110–130.
[22] SCHMIDHUBER J. Deep learning in neural networks：An overview[J]. Neural Networks，2015，61：85–117.
[23] LONG J，SHELHAMER E，DARRELL T. Fully convolutional networks for semantic segmentation[C]// In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [S. l.]：[s. n.]，2015：3 431–3 440.
[24] STAUFFER D，AHARONY A. Introduction to percolation theory[M]. London：Taylor & Francis，2018：93–120.
[25] XUE D，ZHOU J，LIU Y，et al. A strain-based percolation model and triaxial tests to investigate the evolution of permeability and critical dilatancy behavior of coal[J]. Processes，2018，6(8)：127.
[26] SIMONYAN K，ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]// In International Conference on Learning Representations. [S. l.]：[s. n.]，，2015：1–14.
[27] ZHANG X，ZHOU X，LIN M，et al. Shufflenet：An extremely efficient convolutional neural network for mobile devices[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [S. l.]：[s. n.]，2017：1–9.
[28] KRIZHEVSKY A，SUTSKEVER I，HINTON G E. Imagenet classification with deep convolutional neural networks[C]// In Advances in Neural Information Processing Systems. [S. l.]：[s. n.]，2012：1 097–1 105.
[29] EVERINGHAM M，VAN GOOL L，WILLIAMS C K I，et al. The pascal visual object classes (voc) challenge[J]. International Journal of Computer Vision，2010，88(2)：303–338.
[30] SAINATH TN，MOHAMED A，KINGSBURY B，et al. Deep Convolutional Neural Networks for LVCSR[C]// In IEEE International Conference on Acoustics，Speech and Signal Processing. [S .l.]：[s. n.]，2013：8 614–8 618.
[31] ZEILER M D，FERGUS R. Visualizing and understanding convolutional networks[C]// European Conference on Computer Vision. Cham：Springer，2014：818–833.
[32] SELINGRE P. Potrace：a polygon-based tracing algorithm[J]. Potrace (online)，http：//potrace. sourceforge. net/potrace. pdf (2009-07-01)，2003.
[33] MARAGOS P，SCHAFER R. Morphological skeleton representation and coding of binary images[J]. IEEE Transactions on Acoustics，Speech，and Signal Processing，1986，34(5)：1 228–1 244.
[34] ABU-AIN W，ABDULLAH S N H S，BATANINEH B，et al. Skeletonization algorithm for binary images[J]. Procedia Technology，2013，11：704–709.
[35] SMITH A R. Color gamut transform pairs[J]. ACM Siggraph Computer Graphics，1978，12(3)：12–19.
[36] MEZIROW J. Perspective transformation[J]. Adult Education，1978，28(2)：100–110.
[37] FESSLER J. Analytical tomographic image reconstruction methods[J]. Image Reconstruction：Algorithms and Analysis，2009，66：67.
[38] BEISTER M，KOLDITZ D，KALENDER W A. Iterative reconstruction methods in X-ray CT[J]. Physica Medica，2012，28(2)：94–108.
[39] THIBAULT J B，SAUER K D，BOUMAN C A，et al. A three‐dimensional statistical approach to improved image quality for multislice helical CT[J]. Medical Physics，2007，34(11)：4 526–4 544.
[40] BARNSLEY M F. Fractal functions and interpolation[J]. Constructive Approximation，1986，2(1)：303–329.
[41] BARNSLEY M F. Fractals everywhere[M]. Academic Press，2014：48–100.
[42] DENG J，DONG W，SOCHER R，et al. Imagenet：A large-scale hierarchical image database[C]// In 2009 IEEE Conference on Computer Vision and Pattern Recognition. [S. l.]：IEEE，2009：248–255.
[43] KARIMPOULI S，TAHMASEBI P. Image-based velocity estimation of rock using Convolutional Neural Networks[J]. Neural Networks，2019，111：89–97.
[44] ANTLE R. Automated core fracture characterization by computer vision and image analytics of CT images[C]// SPE Oklahoma City Oil and Gas Symposium. [S. l.]：Society of Petroleum Engineers，2019：1–7.
[45] MARQUES V G，DA SILVA L R D，CARVALHO B M，et al. Deep learning-based pore segmentation of thin rock sections for aquifer characterization using color space reduction[C]// In 2019 International Conference on Systems，Signals and Image Processing(IWSSIP). [S.l.]：IEEE，2019：235–240.
[46] KASHIWA T，NAGAI K，TATSUTA H，et al. Development of delamination detection system for concrete decks by using convolutional neural network[C]// Proceedings of the Multidisciplinary Digital Publishing Institute. [S. l.]：[s. n.]，2018：418.