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Detection and characterisation of deep-sea benthopelagic animals from an autonomous underwater vehicle with a multibeam echosounder: A proof of concept and description of data-processing methods

机译:使用多波束回声测深仪从无人水下航行器中检测和表征深海底栖动物:概念证明和数据处理方法描述

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Benthopelagic animals are an important component of the deep-sea ecosystem, yet are notoriously difficult to study. Multibeam echosounders (MBES) deployed on autonomous underwater vehicles represent a promising technology for monitoring this elusive fauna at relatively high spatial and temporal resolution. However, application of this remote-sensing technology to the study of small (relative to the sampling resolution), dispered and mobile animals at depth does not come without significant challenges with respect to data collection, data processing and vessel avoidance. As a proof of concept, we used data from a downward-looking RESON SeaBat 7125 MBES mounted on a Dorado-class AUV to detect and characterise the location and movement of backscattering targets (which were likely to have been individual fish or squid) within 50 m of the seafloor at -800 m similar to depth in Monterey Bay, California. The targets were detected and tracked, enabling their numerical density and movement to be characterised. The results revealed aconsistent movement of targets downwards away from the AUV that we interpreted as an avoidance response. The large volume and complexity of the data presented a computational challenge, while reverberation and noise, spatial confounding and a marginal sampling resolution relative to the size of the targets caused difficulties for reliable and comprehensive target detection and tracking. Nevertheless, the results demonstrate that an AUV-mounted MBES has the potential to proide unique and detailed information on the in situ abundance, distribution, size and behaviour of both individual and aggregated deep-sea benthopelagic animals. We provide detailed data-processing information for thse interested in working with MBES water-column data, and a critical appraisal of the data in the context of aquatic ecosystem research. We consider future directions for deep-sea water-column echosounding, and renforce the importance of measures to mitigate vessel avoidance in studies of aquatic ecosystems.
机译:底栖动物是深海生态系统的重要组成部分,但众所周知其研究难度很大。部署在自动水下航行器上的多波束回声测深仪(MBES)代表了一种有前途的技术,可以在相对较高的时空分辨率下监视这​​种难以捉摸的动物。但是,将这种遥感技术应用于深度较小(相对于采样分辨率),分散和活动的动物的研究在数据收集,数据处理和容器规避方面没有重大挑战。作为概念验证,我们使用了安装在Dorado级AUV上的向下看的RESON SeaBat 7125 MBES的数据,以检测并表征50个范围内后向散射目标(可能是单个鱼或鱿鱼)的位置和运动。 -800 m处的海底m处的深度类似于加利福尼亚州蒙特雷湾的深度。对目标进行检测和跟踪,从而表征其数字密度和运动。结果表明,目标远离AUV向下一致移动,我们将其解释为回避响应。数据的大量和复杂性给计算带来了挑战,而混响和噪声,空间混淆以及相对于目标大小的边际采样分辨率给可靠,全面的目标检测和跟踪带来了困难。然而,结果表明,安装在AUV上的MBES具有提供关于单个和聚集的深海底栖动物的原位丰度,分布,大小和行为的独特而详细的信息的潜力。我们为有兴趣使用MBES水柱数据的人提供了详细的数据处理信息,并在水生生态系统研究的背景下对数据进行了严格的评估。我们考虑了深海水柱回波的未来方向,并在减轻水生生态系统的研究中强调减轻船舶避让措施的重要性。

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