Automated gas bubble imaging at sea floor – a new method of in situ gas flux quantification

Abstract

Abstract. Photo-optical systems are common in marine sciences and have been extensively used in coastal and deepsea research. However, due to technical limitations in the past photo images had to be processed manually or semiautomatically. Recent advances in technology have rapidly improved image recording, storage and processing capabilities which are used in a new concept of automated in situ gas quantification by photo-optical detection. The design for an in situ high-speed image acquisition and automated data processing system is reported ("Bubblemeter"). New strategies have been followed with regards to back-light illumination, bubble extraction, automated image processing and data management. This paper presents the design of the novel method, its validation procedures and calibration experiments. The system will be positioned and recovered from the sea floor using a remotely operated vehicle (ROV). It is able to measure bubble flux rates up to 10 L/min with a maximum error of 33% for worst case conditions. The Bubblemeter has been successfully deployed at a water depth of 1023 m at the Makran accretionary prism offshore Pakistan during a research expedition with R/V Meteor in November 2007.

Time series video analysis of bubble release processes at natural hydrocarbon seeps in the Northern Gulf of Mexico

“…Additional challenges are met when moving from ROV-dependent camera and lighting systems to autonomous systems. Even though more elaborate ROV-dependent camera systems have been used to observe bubbles with higher frame rates, modelled illumination, and smaller lens apertures (Bian et al, 2013;Thomanek et al, 2010;Wang and Socolofsky, 2015;Wang et al, 2016); our results were well within the range that these more elaborate methods measured. For example our bubble size measurements at Mega Plume 1 and Rudyville were comparable (average and standard deviation) to multiple measurements by Wang et al (2016) who used a ROV dependent high-speed stereoscopic imaging system at approximately the same locations (Wang and Socolofsky, 2015) (Fig.…”

“…Furthermore, the SABCA counts bubbles independent of size and was developed to accommodate in-situ positioning of the camera for long term video acquisition. It is adaptable to a variety of settings and does not require a uniform background to facilitate edge detection (Thomanek et al, 2010). This makes our method more versatile for in-situ camera deployments where carefully staged set-ups and high-intensity…”

“…seconds to minutes of video footage (Grant and Whiticar, 2002;Greinert et al, 2010;Leifer, 2010;Leifer and MacDonald, 2003;Römer et al, 2012;Römer et al, 2014;Sahling et al, 2009;Sahling et al, 2014;Valentine et al, 2001). State of the art camera systems have also been developed to capture high definition video for accurate bubble size and velocity quantification (Bian et al, 2013;Thomanek et al, 2010;Wang and Socolofsky, 2015). We argue, however, that longer term deployments (days to months) using autonomous camera systems are necessary to capture important processes such as temporal and spatial variability, biological effects, and observational changes in oil and gas content of bubbles.…”

Observations of bubbles in natural seep flares at MC 118 and GC 600 using in situ quantitative imaging

“…For the size evaluation, we calculated equivalent spherical bubble diameter using ellipse fitting [Leifer and MacDonald, 2003] and area equivalence [Thomanek et al, 2010] approaches. Statistically, both approaches provided consistent result in estimating bubble size, but the equivalent bubble diameter computed using ellipse fitting was systematically lower than that computed with the area equivalence method (with ratio of 0.93:1 for our data) [see Wang and Socolofsky, 2015b].…”

A Model for Variations of Sound Speed and Attenuation from Seabed Gas Emissions

“…where V r [m 3 /s] is the gas flow rate, at which plumes generated can be taken from empirical data (0.1-78 L/min [8], [12], [13], [25], [30]- [33]). Assume the probability of the nth generated bubble is a random value 0 ≤ P (n) ≤ 1, then the nth bubble equilibrium radius R 0 (n) can be obtained by the linear interpolation.…”