Passive acoustics has been identified as an important strategy to determine underwater gas flux at natural sites, or at locations related to anthropogenic activities. The ability of an acoustic system to detect, quantify and locate a gas leak is fundamentally controlled by the Signal to Noise Ratio (SNR) of the bubble sounds relative to the ambient noise. This work considers the use of beamforming methods to enhance the SNR and so improve the performance of passive acoustic systems. In this work we propose a focused beamforming technique to localise the gas seeps. To achieve high levels of noise reduction an adaptive beamformer is employed, specifically the minimum variance distortionless response (MVDR) beamformer. The technique is demonstrated using an array of five hydrophones collecting data at the controlled CO₂ gas release experiment conducted as part of STEMM-CCS (Strategies for Environmental Monitoring of Marine Carbon Capture and Storage) project. The experimental results show that the adaptive beamformer outperforms the conventional (delay and sum) beamformer in undersea bubble localisation. Furthermore, the results with a pair of hydrophone arrays show an improvement of the localisation compared to the use of one hydrophone array.

被动声学已被确定为确定自然地点或与人为活动有关的地点的水下气体通量的重要策略。声学系统检测，量化和定位气体泄漏的能力从根本上由气泡声音相对于环境噪声的信噪比（SNR）控制。这项工作考虑使用波束成形方法来增强SNR，从而改善无源声学系统的性能。在这项工作中，我们提出了一种聚焦波束成形技术来定位气体渗漏。为了实现高水平的降噪，采用了自适应波束形成器，特别是最小方差无失真响应（MVDR）波束形成器。使用五个水听器阵列以受控的CO ₂收集数据来演示该技术气体释放实验是STEMM-CCS（海洋碳捕集与封存环境监测策略）项目的一部分。实验结果表明，在海底气泡定位中，自适应波束形成器优于传统的（延迟和求和）波束形成器。此外，与使用一个水听器阵列相比，一对水听器阵列的结果显示出定位的改善。