Estimating the radii of air bubbles in water using passive acoustic monitoring

TL;DR

This paper presents a Bayesian method combined with a novel feature extraction technique called bubblegram to estimate the radii of underwater air bubbles from acoustic signals, aiding in leak detection.

Contribution

It introduces the bubblegram feature and applies Bayesian inference to improve bubble radius estimation from acoustic data.

Findings

Bubblegram effectively isolates bubble pulse structures.

Bayesian approach improves estimation accuracy.

Laboratory tests validate the method's potential.

Abstract

The study of the acoustic emission of underwater gas bubbles is a subject of both theoretical and applied interest, since it finds an important application in the development of acoustic monitoring tools for detection and quantification of underwater gas leakages. An underlying physical model is essential in the study of such emissions, but is not enough: also some statistical procedure must be applied in order to deal with all uncertainties (including those caused by background noise). In this paper we take a probabilistic (Bayesian) methodology which is well known in the statistical signal analysis communitiy, and apply it to the problem of estimating the radii of air bubbles in water. We introduce the bubblegram, a feature extraction technique graphically similar to the traditional spectrogram but tailored to respond only to pulse structures that correspond to a given physical model. We investigate the performance of the bubblegram and our model in general using laboratory generated data.