Scalable Double Regularization for 3D Nano-CT Reconstruction

TL;DR

This paper introduces a scalable double regularization method for 3D Nano-CT reconstruction that leverages data across slices to improve accuracy and efficiency in imaging shale pore structures, addressing challenges like noise and limited sample size.

Contribution

The proposed SDR method innovatively combines intra-slice total variation and inter-slice L1 regularization for enhanced 3D reconstruction in Nano-CT imaging.

Findings

Outperforms existing methods visually and quantitatively

Effectively utilizes entire dataset for reconstruction

Demonstrated on synthetic and real shale datasets

Abstract

Nano-CT (computerized tomography) has emerged as a non-destructive high-resolution cross-sectional imaging technique to effectively study the sub-$\mu$m pore structure of shale, which is of fundamental importance to the evaluation and development of shale oil and gas. Nano-CT poses unique challenges to the inverse problem of reconstructing the 3D structure due to the lower signal-to-noise ratio (than Micro-CT) at the nano-scale, increased sensitivity to the misaligned geometry caused by the movement of object manipulator, limited sample size, and a larger volume of data at higher resolution. In this paper, we propose a scalable double regularization (SDR) method to utilize the entire dataset for simultaneous 3D structural reconstruction across slices through total variation regularization within slices and $L_1$ regularization between adjacent slices. SDR allows information borrowing both within and between slices, contrasting with the traditional methods that usually build on slice by slice reconstruction. We develop a scalable and memory-efficient algorithm by exploiting the systematic sparsity and consistent geometry induced by such Nano-CT data. We illustrate the proposed method using synthetic data and two Nano-CT imaging datasets of Jiulaodong (JLD) shale and Longmaxi (LMX) shale acquired in the Sichuan Basin. These numerical experiments show that the proposed method substantially outperforms selected alternatives both visually and quantitatively.