Research On Underwater Gas Leak Detection And Transport Flux Measurement Based On The Combination Of Sound And Light

The acoustic detection and estimation of subsea plumes containing methane,formed by natural or anthropogenic activities,make a significant contribution to mitigating global climate change.Additionally,natural gas hydrates,which contain methane gas,serve as one of the indicators for underwater petroleum exploration,highlighting their role in human energy extraction and mining.Therefore,studying the detection and flow estimation of marine plumes holds great significance for both societal and natural development.Utilizing sonar systems for continuous acoustic monitoring of subsea plumes is one of the primary methods to address this issue.In this paper,we investigate the methods for underwater plume detection and flow estimation based on active acoustics.The main contributions of this research are as follows:Firstly,the acoustic scattering characteristics of underwater plumes serve as the theoretical basis for detecting and estimating gas leakage and transport flux.Starting from the acoustic characteristics of individual bubbles within the plume,we quantify the relationship between bubble distribution and the overall scattering strength.Additionally,by incorporating bubble scattering cross-section theory,we establish a model for the backward scattering cross-section of bubbles.Based on the understanding of bubble acoustic properties,we design a laboratory experiment in a water tank that employs a multi-beam sonar system in conjunction with optical devices for simultaneous detection of underwater gas leakage.Secondly,we elucidate the principles of multi-beam sonar imaging in water and utilize the sonar equations to solve the volume scattering intensity of the multi-beam image.By combining the morphological characteristics of gas plumes,we extract features from the target region.We employ Particle Image Velocimetry(PIV)to extract velocity characteristics from the moving region within the multi-beam image.Through the combination of volume scattering characteristics and morphological features,we identify the moving gas plumes and perform three-dimensional reconstruction and skeleton extraction to obtain the actual rising morphology and position of the total gas bubble plume.We estimate the volume scattering intensity of the bubble plume.Thirdly,we process the optical experimental data,including image filtering,enhancement,segmentation,and classifier design,to extract optical bubble feature data and quantify the bubble distribution model.By combining the bubble backward scattering model with the optical bubble feature data,we estimate the volume scattering intensity of the optical bubble plume.Finally,we select the optimal matching model to achieve the integration of acoustic data volume scattering intensity and optical data volume scattering intensity.The matched results,combined with the velocity estimation from the optical experiment data using PIV,enable us to estimate the total gas transport flux.