Auditing Representation-Induced Geometry in Acoustic-Emission Fracture Transients

Abstract

Fixed convolutional representations can induce structured geometry in time-frequency data even without task-specific or source-domain training. We audit a previously reported morphology-space analysis of two granite acoustic-emission experiments. The original cumulative angular-path endpoint is reproduced to within 2.1e-7 in the Archive-B/Archive-A ratio. However, the same directional contrast is obtained with ImageNet features and with three randomly initialized EfficientNet-B0 encoders: the preserved Aether representation gives a ratio of 1.411, ImageNet gives 1.361, and the random encoders give 1.480--1.503. We therefore withdraw the attribution of this contrast to interferometric pretraining. We also correct a reversal between archived OG1/OG3 file identifiers and the physical experiments. Phase randomization and temporal shuffling alter the preserved trajectory, but these responses are reinterpreted as pipeline-dependent diagnostics rather than evidence of a learned universal elastic morphology. The corrected result is narrower: multiple fixed convolutional maps expose a reproducible contrast between two acoustic-emission datasets. The signal statistics and architectural biases responsible for that contrast remain open questions.