Abstract:

This paper proposes a 3D digital rock reconstruction framework based on diffusion prior guidance and multi-scale residual fusion Implicit Neural Representation (INR) to address challenges such as structural discontinuity, topological fracture, and the difficulty in balancing cross-scale microscopic details under sparse 2D slice conditions. The framework introduces the Score Distillation Sampling (SDS) mechanism to transform the geometric and topological priors in pre-trained diffusion models into continuous gradient guidance. It combines the measurement consistency loss for collaborative constraints to achieve the consistent restoration of local fine features and global topological structures under extremely sparse slice constraints. Meanwhile, the framework utilizes multi-scale residual structures to enhance the representation ability of INR for complex pores and improves the generalization performance of the model under different voxel sizes. Experimental results show that this method accurately restores complex pore spaces on various digital rock datasets. The reconstructed structures maintain high consistency with the ground truth in key physical indicators such as porosity distribution and geometric connectivity. In the 256³ scale reconstruction task, the Dice Similarity Coefficient (D_dice) reaches 97.01%, which is 2.6% higher than the baseline INR model. As the reconstruction scale further expands, the D_dice maintains at 95.97% and 92.88% under 512³ and 1024³ high voxel size tasks, respectively, demonstrating excellent stability in large-scale reconstruction. In the cross-sample generalization tests for Berea sandstone and Ketton limestone, the D_dice reaches 93.44% and 95.51%, respectively. This study solves the problem of stable reconstruction for complex porous media in data-limited scenarios and provides a physically reliable and continuous new technical solution for refined geological modeling.

摘要： 针对稀疏二维切片条件下数字岩心重建易出现的结构不连续、拓扑断裂及跨尺度微观细节难以兼顾等挑战，提出一种基于扩散先验引导与多尺度残差融合隐式神经表示（Implicit Neural Representation, INR）的数字岩心三维重建框架。该框架通过引入得分蒸馏采样（Score Distillation Sampling, SDS）机制，将预训练扩散模型中蕴含的几何与拓扑先验转化为连续梯度引导，并结合测量一致性损失进行协同约束，旨在实现极稀疏切片约束下局部精细特征与全局拓扑结构的一致性恢复。同时，利用多尺度残差结构增强INR对复杂孔隙的表达能力，提升了模型在不同体素尺寸下的泛化表现。实验结果表明，该方法在多种数字岩心数据集上均能准确还原复杂的孔隙空间，其重建结构在孔隙率分布和几何连通性等关键物理指标上与真实值保持高度一致。在256³规模的重建任务中，Dice相似系数（Dice Similarity Coefficient，Ddice）达到97.01%，相较于基础INR模型提升了2.6%。随着重建尺度进一步扩大，在512³与1024³高体素尺寸任务下，Ddice仍能分别维持在95.97%和92.88%，展现出优异的大尺度重建稳定性。在针对Berea砂岩与Ketton石灰岩的跨样本泛化性测试中，Ddice分别达到93.44%与95.51%。该研究解决了复杂多孔介质在数据受限场景下的稳定性重建问题，为精细化地质建模提供了一种物理可靠且连续化的新型技术方案。