Deep learning-based characterization of underwater methane bubbles using simple dual camera platform

Abstract

Seabed gas and oil emissions appear as bubble plumes ascending through the water column in various environments. Understanding bubble characteristics—size, rise speed—is important for estimating escape rates of fluids like methane, oil, and carbon dioxide. However, measuring underwater gas bubbles is challenging, often requiring expensive specialized equipment. This study presents a novel methodology using two calibrated consumer-grade cameras to estimate bubble size distribution, rise velocities, and corresponding gas or oil flow rates. Our approach, named BURST (Bubble Rise and Size Tracking), uses a trained neural network for automated bubble detection in diverse camera footage, effectively analyzing under varying lighting conditions and visibility, without requiring a uniform backlit background for bubble identification. Post-detection, bubbles are tracked and synchronized between the cameras, with three-dimensional triangulation used to deduce sizes and rise speeds, enabling flow rate calculations. We demonstrate the efficacy of our methodology through basin experiments capturing methane bubble plumes with controlled flow rates. Additionally, we successfully apply this methodology to existing footage from natural methane emission sites in the Hopendjupet seeps within the central Barents Sea, measuring methane flow rates of approximately 46 and 24 mmol CH₄ min⁻¹ at water depths of 327 and 341 m, respectively. These results underscore the practical applicability of BURST in complex underwater environments without disrupting natural bubble flow. By utilizing readily available equipment, BURST enables reliable bubble measurements in challenging real-world conditions, including the analysis of legacy footage not initially intended for bubble flow rate quantification. The BURST python script is available at https://github.com/BUbbleRST/BURST/.