Authenticity Verification of Acoustic Leak-Survey Recordings from Signal Microstructure

In acoustic non-destructive testing (NDT) of pipelines, leak localization relies on computing the cross-correlation between two pulse-code-modulated (PCM) sensor signals and estimating the time delay of arrival. Because a standard correlator evaluates only phase coherence and passband spectral shape, a software-synthesized WAVE file with an imposed inter-channel delay is accepted by conventional diagnostic software as a valid field measurement. This study investigates the capabilities and operational limits of blind authenticity verification for acoustic leak-survey recordings based solely on the digital sample sequence without external hardware metadata. Using a benchmark corpus of 1,588 unique 16-bit PCM recordings (921 verified field recordings from correlators, hydrophones, and acoustic noise loggers; 667 synthesized waveforms across 11 parametric generator families), we evaluate 28 physical and hardware-level signal features, including spectral rolloff near the Nyquist frequency, analog-to-digital converter (ADC) least-significant-bit (LSB) parity, 50 Hz power-line harmonic contrast, temporal root-mean-square (RMS) energy variation, and inter-channel coherence. Under stratified five-fold cross-validation, a decision-tree ensemble separates field and synthetic recordings with an area under the receiver operating characteristic curve (ROC-AUC) of 1.000. Feature-contribution analysis demonstrates that classification is driven primarily by anti-aliasing filter rolloff near the Nyquist limit (34.3%), channel configuration (12.3%), and ADC word-length parity (9.5%), whereas 50 Hz power-line harmonics contribute 4.1%. However, when evaluated on the withheld Advanced Physical Leak generator family, recall drops to 0.61 at the 0.50 cutoff and to 0.00 at the 0.75 cutoff due to score compression, while passing 118 withheld synthetic signals through a second-order Butterworth low-pass filter at 0.97 of the Nyquist frequency reduces recall at the 0.50 cutoff from 0.941 to 0.000. These results demonstrate that statistical authenticity detectors rely heavily on ADC anti-aliasing and container characteristics, which must be accounted for when designing secure NDT verification pipelines.