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| one publication added to basket [336514] |
Processing of multibeam water column image data for automated bubble/seep detection and repeated mapping
Urban, P.; Köser, K.; Greinert, J. (2017). Processing of multibeam water column image data for automated bubble/seep detection and repeated mapping. Limnol. Oceanogr., Methods 15(1): 1-21. https://dx.doi.org/10.1002/lom3.10138
In: Limnology and Oceanography: Methods. American Society of Limnology and Oceanography: Waco, Tex.. ISSN 1541-5856; e-ISSN 1541-5856, meer
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| Auteurs | | Top |
- Urban, P., meer
- Köser, K.
- Greinert, J., meer
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| Abstract |
Water 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. |
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