A unified framework for morphological and intra-particle pores reconstruction in calcareous sand using spherical harmonics and random field methods

Abstract

This study presents a high-fidelity digital reconstruction framework for calcareous sand particles, which enables large-scale stochastic generation of particle morphologies and intra-particle pore structures with controllable statistical properties. By integrating spherical harmonic decomposition and gaussian random field modeling, the proposed method decomposes particle geometry into macro-scale shape, micro-scale roughness, and intra-particle porosity components. The spherical harmonic–based controllable scale morphological reconstruction approach is developed to separately reconstruct global morphology and surface roughness, with the latter generated via high-order perturbations and random field techniques. A principal component analysis–based shrinkage–reversion strategy is introduced to mitigate shape distortion in anisotropic particles. For intra-particle pore modeling, a spectral random field method is employed, coupled with multi-scale gaussian filtering and error-feedback optimization to regulate pore size distributions and spatial correlations. Morphological evaluations show that the generated particles maintain deviations within ± 10 % in shape descriptors and ± 20 % in roughness parameters, while preserving statistical features such as intra-particle porosity and fractal dimension. The method enables the generation of statistically diverse particle sets based on limited CT samples, offering scalable and realistic geometric inputs for discrete element modeling and multi-physics simulations in porous granular media.