Abstract
AbstractWater Column Imaging Multibeam Echosounder Systems (MBES) are effective and sensitive tools for investigating free gas (bubble) release and its rise through the water column. The main advantages of MBES are the detection range and lateral coverage in the water column and at the seafloor; furthermore, they are becoming increasingly available on research vessels worldwide. However, high noise levels and systematic artefacts due to side‐lobe induced signal interference degrade MBES Water Column Images (WCIs) and hampered automated bubble detection and related gas seepage investigations. We present a new technique advancing automated detection of bubble streams and moving toward a quantitative gas‐release assessment. It is shown that bubble streams can be detected reliably by their spatio‐temporal behavior even when they are discontinuous in WCI data. Using assumptions about the bubble rising trajectories, bubble release spots at the seafloor can be traced even if the source location is obscured by acoustic noise or unwanted acoustic targets. A map with acoustic response and source locations of bubbles being released can be produced and serves as a starting point for more detailed quantitative analyses. The efficiency of the method has been assessed at a methane seep site in the Dutch North Sea. Multiple survey lines are merged to a detailed acoustic map of the area. Processed results are in good agreement with manual investigations of the WCI data as well as ROV‐based video analysis.
Highlights
Water Column Imaging Multibeam Echosounder Systems (MBES) are effective and sensitive tools for investigating free gas release and its rise through the water column
Multibeam Echosounder Systems (MBES) surpass single- and split-/beam systems in this regard as they ensonify a swath below the ship, typically covering 1208, resulting in a significantly wider coverage per survey line
We advocate automated machine processing and evaluation of survey data, where algorithms and processing parameters can be published and shared, facilitating a better reproducibility of dataset analysis. We introduce such a reproducible and effective processing routine for data filtering, bubble release identification and actual bubble vent localization at the seafloor in Water Column Images (WCIs) data
Summary
Masking and excluding static acoustic distortions We propose two different methods for excluding static acoustic distortions. The background AV level (BV), which includes the systematic side-lobe distortions, can be estimated by stacking consecutive WCIs and calculating the median signal level for each acoustic sample in the beams. We suggest that the median filter length (the number of consecutive pings/WCIs used to create the median image) should be three times the number of pings that ensonified the widest detected flare to avoid the influence of bubble-related signals on the median stack image. The bubble displacement can be extracted from an interactively detected strong flare acting as a “template” (Fig. 6). To calculate the bubble displacement from such a flare, the corresponding data points are extracted into 3D space. The 2D bubble displacement vector ~b~z for each ~z is calculated to a center of mass, but with respect to the volume backscattering strength values AV;i. for all Nd points inside discrete depth ranges:
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