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首页> 外文期刊>ICES Journal of Marine Science >Sensitivity analysis and parameter selection for detecting aggregations in acoustic data
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Sensitivity analysis and parameter selection for detecting aggregations in acoustic data

机译：用于检测声学数据中聚合的灵敏度分析和参数选择

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A global sensitivity analysis was conducted on the algorithm implemented in the Echoview ® software to detect and describe aggregations in acoustic backscatter. Multiple aggregation detections were performed using walleye pollock (Theragra chalcogramma) data from the eastern Bering Sea. Walleye pollock form distinct aggregations and dense and diffuse layers. In each aggregation detection, input parameters defining minimum size, density, and distance to other aggregations were selected at random using a Latin hyper-cube sampling design. Sensitivity was quantified by testing for correlation among input parameters and a series of aggregation descriptors. In all, 336 correlation tests were performed, corresponding to a combination of seven detection input parameters, eight aggregation descriptors, and six transects. Among these, 181 tests were significant, indicating sensitivity between input parameters and aggregation descriptors. The aggregation-detection algorithm is sensitive to changes in threshold and minimum size, but less sensitive to changes in the connectivity criterion among aggregations.
机译：对在Echoview®软件中实施的算法进行了全局灵敏度分析，以检测和描述声学反向散射中的聚集。使用来自白令海东部的角膜鳕（Theragra chalcogramma）数据进行了多次聚集检测。角膜白鲸形成明显的聚集体以及密集和弥散的层。在每个聚集检测中，使用拉丁超立方体采样设计随机选择定义最小大小，密度和与其他聚集的距离的输入参数。通过测试输入参数与一系列聚合描述符之间的相关性来量化灵敏度。总共进行了336个相关测试，分别对应于七个检测输入参数，八个聚合描述符和六个样线的组合。其中，有181个测试具有显着性，表明输入参数和聚合描述符之间的敏感性。聚合检测算法对阈值和最小大小的更改敏感，但对聚合之间的连通性标准的更改较不敏感。

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