Enhanced multi-leak detection in pressurized pipelines using super-resolution matched-field processing

The detection of a cluster of leaks in pressurized water supply pipelines is crucial as it allows for early recognition of the pipeline's poor overall condition, enabling timely interventions. This paper addresses this challenge by extending, for the first time, a high-resolution modified broadband matched-field processing (MB-MFP) framework to the multi-leak detection problem. The developed methods combine advanced signal processing with acoustic measurements to achieve super-resolution identification, where leaks spaced closer than half the minimum probing wavelength are successfully resolved. A key contribution is a methodology that enables the simultaneous estimation of the number, locations, and sizes of multiple leaks. We also show that the techniques are robust to various noise types and levels, including white noise with a signal-to-noise ratio (SNR) as low as -10 dB, as well as colored and impulse noise. The effectiveness and robustness of the proposed techniques are demonstrated through comprehensive numerical simulations and validated with laboratory experiments.