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Practical calibration of ship-mounted omni-directional fisheries sonars

机译:船用全向声纳的实用校准

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摘要

Sonars and echosounders are widely used for remote sensing of life in the marine environment. There is an ongoing need to make the acoustic identification of marine species more correct and objective and thereby reduce the uncertainty of acoustic abundance estimates. In our work, data from multi-frequency echosounders working simultaneously with nearly identical and overlapping acoustic beams are processed stepwise in a modular sequence to improve data, detect schools and categorize acoustic targets by means of the Large Scale Survey System software (LSSS). Categorization is based on the use of an acoustic feature library whose main components are the relative frequency responses. The results of the categorization are translated into acoustic abundance of species. The method is tested on acoustic data from the Barents Sea, the Norwegian Sea and the North Sea, where the target species were capelin (Mallotus villosus L.), Atlantic mackerel (Scomber scombrus L.) and sandeel (Ammodytes marinus L.), respectively. Manual categorization showed a high conformity with automatic categorization for all surveys, especially for schools.
机译:声纳和回声测深仪广泛用于遥感海洋环境中的生命。持续需要使海洋物种的声学识别更加正确和客观,从而减少声学丰度估计的不确定性。在我们的工作中,来自多频回声测深仪的数据与几乎相同且重叠的声束同时工作,并通过模块化序列逐步进行处理,以借助大型测量系统软件(LSSS)来改善数据,检测学校并对声目标进行分类。分类是基于声学特征库的使用,其主要成分是相对频率响应。分类的结果被转换为物种的声音丰度。该方法已经在来自巴伦支海,挪威海和北海的声学数据上进行了测试,目标物种是毛鳞鱼(Mallotus villosus L.),大西洋鲭鱼(Scomber scombrus L.)和sandeel(Ammodytes marinus L.),分别。手动分类显示与所有调查(尤其是学校)的自动分类高度一致。

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