Research On Underwater Gas Leak Detection Method Based On Bubble Acoustic

Underwater gas leak widely exists in natural world and industry especially in the ocean.The underwater gas leak formed by the gas pipeline rupture,cold seep,gas hydrates mining and the failure of CSS will result economic losses as well as safety and environment problem which lead to a baleful influence in the ocean exploitation.The application of underwater gas leak detection technique can detect the leakage in time to avoid severe consequences by repairing the rupture as a remedy.Therefore,the research on underwater gas leak detection technique is of great meaning.A leak detection method is proposed by recognizing the bubble acoustic signals generated by the leakage according to the bubbling phenomenon caused by the underwater gas leak.The method can be applied in most occasions and has a high sensitivity as its advantages which may be an important supplement of existing techniques.The bubble is elastic for the compressibility of its inner gas so it will pulsate when stimulated by an initial excitation and generates acoustic signals in the liquid.The dynamical model of pulsating bubble and the propagation and attenuation of the acoustic signal are analyzed and discussed.A simulation platform of underwater gas leak is built to study the formation of bubbles under various flow rates of gas leak by high-speed photography.The attribution of sound generation is confirmed by introducing the time-frequency analysis and the “pinch-off”,coalescence and splitting of bubbles would provide the initial energy of bubble pulsation.The production of bubble is nearly periodical and repeatable in a small flow rate of gas leak.A new method based of time-frequency energy analysis and adaptive detecting algorithm of harmonics are proposed according to the features which can recognize the bubble acoustic signal and the bubble production rate with a better noise resistance than traditional signal processing methods.The influences of the length of window function,noise intensity and attenuation coefficient on the algorithm performance are also analyzed by confidence analysis.The proposed algorithm has more resistance to noise than traditional frequency domain methods.An algorithm based on stochastic resonance in a bistable system and quantum particle swarm optimization is also proposed to recognize the position of the bubble acoustic signal submerged in noise.The pulse-like signal is selected as the optimal stochastic resonance output and quantum particle swarm optimization is applied in a bistable system to realize the algorithm.Moreover,an algorithm based on reverse stochastic resonance is proposed to improve the accuracy of position recognition which benefits the localization of leakage.With the increase of the gas leak flow rate,the acoustic signal is of continuity so the source enumeration methods based on array signal processing can be applied to recognize its existence.A performance analysis on the MDL criterion for source enumeration in array processing is presented which is more accurate than available methods especially in small number of snapshots.The proposed method can estimate the probability of different enumeration results and analyze the extent of enumeration underestimation which is instructive for the design and parameter selection of sensor array for gas leak detection in underwater environments.