A CFAR Detection Approach for Identifying Gas Bubble Seeps With Multibeam Echo Sounders
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A CFAR Detection Approach for Identifying Gas Bubble Seeps With Multibeam Echo Sounders

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  • Alternative Title:
    A cell-averaged constant false alarm rate (CFAR) detector is described and applied to data collected with a multibeam echo sounder (MBES). The CFAR detector is designed specifically for transient targets observed with MBES, and operates under the assumption that background noise, including volume and seafloor reverberation, is locally stationary in time. This assumption, and the CFAR detector performance in general, was examined for data collected by a 30-kHz MBES operating in the Gulf of Mexico where the targets of interest were methane gas bubble plumes rising up from the seabed. Results with example data suggest that the CFAR detector was able to remove 99.1% of the MBES raw data while preserving the targets of interest. False detections appear randomly distributed throughout a single MBES ping, unlike the targets, and a within-ping target clustering algorithm was able to remove many of the false detections. In a single ping, an example is shown where the combined CFAR detector and a target cluster-size rule was able to reduce the number of false detections to 99.8% of the original data. The detector and cluster-size rules were applied to a sequence of approximately 400 pings, and two additional morphological rules based on the size and aspect ratio of the resulting target clusters were then applied to the detections to isolate the MBES backscatter intensity associated with gas bubbles. This combination of CFAR detector and simple morphological classification rules provides a useful way to detect gas bubble seeps or other transient targets.
  • Journal Title:
    IEEE Journal of Oceanic Engineering
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  • Description:
    A cell-averaged constant false alarm rate (CFAR) detector is described and applied to data collected with a multibeam echo sounder (MBES). The CFAR detector is designed specifically for transient targets observed with MBES, and operates under the assumption that background noise, including volume and seafloor reverberation, is locally stationary in time. This assumption, and the CFAR detector performance in general, was examined for data collected by a 30-kHz MBES operating in the Gulf of Mexico where the targets of interest were methane gas bubble plumes rising up from the seabed. Results with example data suggest that the CFAR detector was able to remove 99.1% of the MBES raw data while preserving the targets of interest. False detections appear randomly distributed throughout a single MBES ping, unlike the targets, and a within-ping target clustering algorithm was able to remove many of the false detections. In a single ping, an example is shown where the combined CFAR detector and a target cluster-size rule was able to reduce the number of false detections to 99.8% of the original data. The detector and cluster-size rules were applied to a sequence of approximately 400 pings, and two additional morphological rules based on the size and aspect ratio of the resulting target clusters were then applied to the detections to isolate the MBES backscatter intensity associated with gas bubbles. This combination of CFAR detector and simple morphological classification rules provides a useful way to detect gas bubble seeps or other transient targets.
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  • Source:
    IEEE Journal of Oceanic Engineering, 1-10
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    CC BY
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    Submitted
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