Reconstruction of granite microstructure model using simulated annealing method and Voronoi tessellation

Abstract

PurposeAccurate presentation of the rock microstructure is critical to the grain-scale analysis of rock deformation and failure in numerical modelling. 3D granite microstructure modelling has only been used in limited studies with the mineral pattern often remaining poorly constructed. In this study, the authors developed a new approach for generating 2D and 3D granite microstructure models from a 2D image by combining a heterogeneous material reconstruction method (simulated annealing method) with Voronoi tessellation.Design/methodology/approachMore specifically, the stochastic information in the 2D image is first extracted using the two-point correlation function (TPCF). Then an initial 2D or 3D Voronoi diagram with a random distribution of the minerals is generated and optimised using a simulated annealing method until the corresponding TPCF is consistent with that in the 2D image. The generated microstructure model accurately inherits the stochastic information (e.g. volume fraction and mineral pattern) from the 2D image. Lastly, the authors compared the topological characteristics and mechanical properties of the 2D and 3D reconstructed microstructure models with the model obtained by direct mapping from the 2D image of a real rock sample.FindingsThe good agreements between the mapped and reconstructed models indicate the accuracy of the reconstructed microstructure models on topological characteristics and mechanical properties.Originality/valueThe newly developed reconstruction method successfully transfers the mineral pattern from a granite sample into the 2D and 3D Voronoi-based microstructure models ready for use in grain-scale modelling.

A U-Net-based self-stitching method for generating periodic grain structures

“…There are many existing methods to generate periodic structures from scratch [7]. Among those, Voronoibased method, metaheuristic approaches, and models built on physical laws are extensively discussed [8][9][10]. However, the generation of periodic boundaries comparable with reference structures is less visited.…”

“…The desired interpolation should go beyond the direct truncation, the air layer [11], and the polynomial interpolation [12,13]. Previous works try to rebuild microstructures based on geometrical rules [8] or by using meta-heuristic methods [9], requiring additional assumption of the properties of microstructure rather than depending on the intrinsic information contained by the sample them self. In comparison to traditional approaches, neural networks are successful and powerful tools that meet such requirements.…”