When Physical Views Should Not Be Positive Pairs: Cross-Frequency Collapse in Self-Supervised Learning for Eddy Current Testing

Many scientific instruments acquire co-registered channels through distinct measurement operators, making cross-channel positive pairing tempting in self-supervised learning. Correspondence, however, is not exchangeability: pairing declares the operator difference nuisance for the task. When that difference carries target signal, pairing can suppress information the task needs. We test this failure mode in multi-frequency eddy current testing (ECT), where frequency controls penetration and the operator difference is known from physics. Holding backbone, objective, augmentation policy, split, and evaluation fixed, we change only the second-view channel. Same-frequency pretraining reaches 0.69–0.71 macro-AUPRC against a 0.047 random-ranking reference; all nine cross-frequency comparisons degrade transfer, with penalties approximately 5%–20% of their matched same-frequency macro-AUPRC, grouped physical-unit support after multiplicity correction, and adverse directions in all three pretraining seeds. The effect spans VICRegL, DenseCL, and SimCLR: mechanisms that avoid trivial collapse do not guarantee preservation of task-relevant operator variation when the view distribution encourages invariance to it. LP-FT shrinks the mean gap from 0.0688 to 0.0222 under full labels, bounding cross-frequency collapse to a supervision-dependent transfer penalty. PhysAug provides the constructive counterpart by deriving views from known sensing nuisances. Thus, when operator and target dependence are sufficiently known, candidate positive pairs can be audited at design time. Our controlled ECT study therefore motivates a broader principle for multi-channel scientific sensing: positive-pair validity depends on whether operator-dependent variation is nuisance or downstream signal, not merely on physical correspondence.