Quantum eddy current wall thickness tomography using nitrogen-vacancy vector magnetometry for onshore crude oil pipeline integrity assessment

As core infrastructure for global energy distribution, onshore long-distance crude oil pipelines face multiple challenges, including wax deposition, structural stress concentration, and H 2 S release. Traditional non-destructive testing (NDT) techniques, such as eddy current testing and magnetic flux leakage (MFL), suffer from limitations including low spatial resolution, limited penetration depth, and directional blind spots, making high-resolution and omnidirectional defect identification difficult. Diamond nitrogen-vacancy (NV) centers have shown strong potential for high-precision vector magnetic sensing. However, their application in pipeline inspection remains limited by insufficient environmental adaptability, the lack of quantitative wall-thickness inversion models, and difficulties in large-scale sensor array integration. To address these issues, this study develops a hybrid framework combining NV vector magnetometry with quantum eddy-current tomography. Directional dependence is substantially reduced through a three-phase orthogonal excitation field, as verified by controlled comparisons with single-phase and dual-phase excitation, while deterministically laser-written NV centers integrated with metal lenses enable sensor array implementation. A machine-learning-assisted tomographic reconstruction algorithm is further proposed to convert discrete magnetic measurements into continuous wall-thickness images. The results show a detection accuracy of 61.0% for a controlled 1 μm nominal wall-thickness perturbation and a coherence time of 70.5 μs under 25 nT noise. In the matched field evaluation, the proposed system achieved an overall detection rate of 93.5%, compared with 65.6% for MFL under the tested conditions, while ultrasonic calibration indicated close agreement between reconstructed and reference thicknesses. These findings provide preliminary evidence for the feasibility of NV-based quantitative pipeline inspection under the evaluated conditions.