FCN-based intelligent identification and fractal reconstruction of pore-fracture network in coal bymicro CT scanning

Digital core establishment, as an ideal model to study the physical and mechanical properties of rock, provides an undifferentiated numericalsimulation. However, high level of accurate and efficient modeling restrictsthe promotion of digital core reconstruction technology. The traditional methodsare 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 enhancedbased on micro CT scanning, anda 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, andmore importantly, the accuracy of intelligent recognition will continue to be improvedwith 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. © 2020, Science Press. All right reserved.