Quantum Magnetometers for Infrastructure Inspection and Monitoring

Abstract

Damage in infrastructure is often hidden until it becomes costly or dangerous. Common examples include corrosion under insulation, early fatigue damage in steel, corrosion of embedded reinforcement, and abnormal current flow in batteries and power equipment. Magnetic methods are attractive because they can sense through coatings, insulation, and concrete cover without couplants, but field performance is often limited by lift-off, low-frequency drift, background magnetic noise, and the weak low-frequency response of pickup coils. This review examines two room-temperature quantum receiver platforms: optically pumped atomic magnetometers (OPMs) and nitrogen-vacancy (NV) diamond magnetometers. Rather than treating them as stand-alone sensors, we compare them as parts of a full measurement chain that includes source physics, geometry, readout, calibration, and interpretation. The literature is organized into four magnetic signal classes: driven induction responses, leakage fields in magnetic flux leakage inspection, passive self-fields linked to stress or corrosion, and fields produced by operational currents. OPMs are strongest for low-frequency, phase-referenced induction measurements, while NV sensors are strongest for near-surface field mapping, vector or gradient measurements, and differential current sensing in compact solid-state heads. Across all applications, deployment depends less on best-case sensitivity than on usable bandwidth, dynamic range, background rejection, geometry control, calibration, and validation. The clearest path to field use is therefore robust instrument engineering tied to qualification methods that reflect real inspection conditions.

Figures (13)

• Figure 1: Infrastructure magnetometry overview. Left: representative hidden-damage cases that motivate sensing at stand-off $h$, including corrosion under insulation (CUI) on steel, corroded rebar in concrete, and current redistribution in batteries or busbars. Top: two field workflows---inspection (a one-time scan to support a repair decision) and monitoring (repeatable measurements to track change). Bottom: receiver-centric measurement chain and the four recurring source classes used throughout this review: driven induction responses, leakage fields, passive self-fields, and operational-current fields. The measured field $B(\mathbf{r},t)$ is filtered by stand-off and orientation, converted by the receiver, and processed by readout (lock-in detection or resonance tracking) into a decision metric with uncertainty. Key field constraints include 50/60 Hz interference and moving magnetic clutter, low-frequency drift/$1/f$ noise, and the reduced low-frequency response of inductive pickup ($\propto d\Phi/dt$).
• Figure 2: Operating principle of an alkali-vapor magnetometer. (a) Circularly polarized pump light spin-polarizes an alkali vapor (Rb, Cs, or K), producing a macroscopic magnetization aligned with the pump axis or a chosen bias field $B_0$. (b) Changes in magnetic field drive spin precession, which is read out optically, commonly through probe-beam polarization rotation and polarimetric detection. For field deployment, the same head typically includes bias and compensation coils that define the operating point and help reject ambient background fields.
• Figure 3: Diamond NV-center magnetometer. (a) Magnetic-field projection onto the NV axis, so the relevant measured quantity is the axial component $B_{\parallel}=\mathbf{B}\cdot\hat{\mathbf{u}}_{\mathrm{NV}}$. (b) Optical initialization and spin-dependent fluorescence readout. Green excitation prepares the spin toward the $m_s=0$ state, while the fluorescence depends on spin state. (c) ODMR resonance shift under magnetic field: Zeeman splitting separates the resonances to $f_{-}$ and $f_{+}$, while common-mode shifts can move both resonances together. (d) Differential tracking of the split resonances provides a robust magnetic-field output while suppressing temperature-driven and other nuisance shifts.
• Figure 4: Atomic-magnetometer electromagnetic induction imaging (EMI). (a) A drive coil generates the primary field and induced eddy currents; the magnetometer detects the secondary magnetic response. (b--e) Representative lock-in amplitude and phase maps over aluminum plates with concealed recesses and cavities. (f,g) Frequency-dependent contrast for different defect depths, illustrating tunable depth sensitivity through the choice of excitation frequency bevington2020inductive.
• Figure 5: Atomic-magnetometer EMI of concealed metal loss. (a,b) Steel plate measurements showing phase and amplitude contrast for recesses of increasing depth Bevington2018Steelwork. (c,d) Insulated aluminum specimen with recesses and a through-cut defect, with frequency-dependent contrast used to tune depth sensitivity maddox2022imaging.