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基于三维相空间法和稳健估算法的ADV流速数据后置处理效果研究

钟纯怿 张俊波 杨振昊 季舒恒 万荣

钟纯怿,张俊波,杨振昊,等. 基于三维相空间法和稳健估算法的ADV流速数据后置处理效果研究[J]. 海洋学报,2021,43(8):152–159 doi: 10.12284/hyxb2021132
引用本文: 钟纯怿,张俊波,杨振昊,等. 基于三维相空间法和稳健估算法的ADV流速数据后置处理效果研究[J]. 海洋学报,2021,43(8):152–159 doi: 10.12284/hyxb2021132
Zhong Chunyi,Zhang Junbo,Yang Zhenhao, et al. Research on the detection effect of post-processing method of Acoustic Doppler Velocimetry data based on three-dimensional phase space thresholding method and robust estimation method[J]. Haiyang Xuebao,2021, 43(8):152–159 doi: 10.12284/hyxb2021132
Citation: Zhong Chunyi,Zhang Junbo,Yang Zhenhao, et al. Research on the detection effect of post-processing method of Acoustic Doppler Velocimetry data based on three-dimensional phase space thresholding method and robust estimation method[J]. Haiyang Xuebao,2021, 43(8):152–159 doi: 10.12284/hyxb2021132

基于三维相空间法和稳健估算法的ADV流速数据后置处理效果研究

doi: 10.12284/hyxb2021132
基金项目: 国家重点研发计划项目(2019YFC0312104);上海市青年东方学者项目(QD2017038)。
详细信息
    作者简介:

    钟纯怿(1994‒),女,上海市人,博士研究生,研究方向为物理海洋学。E-mail:chunyiz0906@sina.com

    通讯作者:

    万荣,男,教授,主要从事渔具理论与设计、离岸养殖设施水动力学、渔业资源评估与管理等研究。E-mail:rwan@shou.edu.cn

  • 中图分类号: S951.2

Research on the detection effect of post-processing method of Acoustic Doppler Velocimetry data based on three-dimensional phase space thresholding method and robust estimation method

  • 摘要: 水流是影响藤壶、牡蛎、藻类等水生生物能否成功附着在渔业装备上的重要环境因素,水体流速的变化会改变藻类新陈代谢过程,制约藤壶与牡蛎幼虫大面积扩散。流速测量数据的准确性对研究渔业装备上附着生物的附着机制起到至关重要的作用。声学多普勒流速仪(ADV)是测量水体流速的重要设备。然而,水体中的气泡、大颗粒悬浮物等因素会影响ADV的相关系数和信噪比,从而导致流速数据中产生野点,降低了数据的准确性。本文基于国内外主流算法稳健估算法与三维相空间法,以ADV实际测量的流速数据为例,对比研究两种后置处理方法的检测效果。结果显示,稳健估算法平均检出率为4.95%,峰度系数平均下降77.71%,三维相空间法平均检出率为14.60%,峰度系数平均下降84.05%。综合分析,三维相空间法处理效果较好,其检测准确性高于稳健估算法,但存在过处理现象。建议在处理野点含量低于5%的数据时选用相空间法,而对于野点偏离样本平均值较远的数据时,可以选用稳健估算法。本文对减小流速数据误差,为准确定量研究水流对渔业装备附着生物的附着量与种类影响有着重要的意义。
  • 图  1  研究区域与流速监测站点

    Fig.  1  Study area and velocity monitoring site

    图  2  站点A水面下30 cm处流速数据

    Fig.  2  Flow velocity data at 30 cm below the water surface at Station A

    图  3  站点A水面下30 cm处稳健估算法检测结果

    a. 速度分量u流速数据检测结果;b. 速度分量v流速数据检测结果;c. 速度分量w流速数据检测结果

    Fig.  3  The detection results of the robust estimation method at 30 cm below the water surface of Station A

    a. Velocity data detection result in u; b. velocity data detection result in v; c. velocity data detection result in w

    图  4  站点A水面下30 cm处三维相空间法检测结果

    a. 速度分量u流速数据检测结果;b. 速度分量v流速数据检测结果;c. 速度分量w流速数据检测结果

    Fig.  4  The detection results of the three-dimensional phase space thresholding method at 30 cm below the water surface of Station A

    a. Velocity data detection result in u; b. velocity data detection result in v; c. velocity data detection result in w

    图  5  站点A离底20 cm处数据处理效果对比

    a. 速度分量u流速数据检测结果;b. 速度分量v流速数据检测结果;c. 速度分量w流速数据检测结果

    Fig.  5  Comparison of data processing effects at 20 cm above the bottom of Station A

    a. Velocity data detection result in u; b. velocity data detection result in v; c. velocity data detection result in w

    表  1  站点A水面下30 cm处流速数据统计参数值

    Tab.  1  Statistical parameter values of flow velocity data at 30 cm below the surface of Station A

    方法名称速度分量平均值/(m·s−1)标准偏差峰度系数偏斜系数最大值/(m·s−1)最小值/(m·s−1)
    实测数据u−0.007 7210.023 84516.910 402−1.113 3320.089 9−0.147 9
    v−0.011 9870.030 56227.141 5933.214 1570.205 3−0.107 6
    w0.001 5760.025 55523.118 6443.021 3040.178 3−0.079 6
    稳健估算法u−0.007 0510.011 1423.255 587−0.162 1350.023 2−0.038 3
    v−0.014 7980.017 7097.712 472−1.497 7700.018 5−0.092 8
    w−0.001 4150.015 8464.440 737−0.877 8040.029 8−0.059 1
    三维相空间法u−0.007 2380.009 2762.101 112−0.011 2010.009 6−0.029 0
    v−0.011 0540.012 8812.448 1170.099 9700.017 0−0.040 3
    w0.003 1310.017 9456.105 6871.342 9660.068 2−0.027 6
    下载: 导出CSV

    表  2  站点A离底20 cm处流速数据统计参数值

    Tab.  2  Statistical parameter values of flow velocity data at 20 cm above the bottom of Station A

    方法名称速度分量平均值/(m·s−1)标准偏差峰度系数偏斜系数最大值/(m·s−1)最小值/(m·s−1)
    实测数据u0.002 4850.003 43828.518 1380.539 1320.048 2−0.039 2
    v−0.000 9830.003 13718.550 2310.103 7490.030 2−0.042 1
    w−0.001 3420.001 29318.826 0660.127 4270.009 4−0.015 1
    稳健估算法u0.002 4640.002 6413.500 8890.089 8150.016 7−0.006 3
    v−0.001 0150.002 6072.824 9610.155 6290.008 4−0.007 8
    w−0.001 3300.000 9083.817 821−0.163 7200.002 2−0.004 8
    三维相空间法u0.002 4860.002 7143.615 6970.032 6470.014 2−0.008 0
    v−0.001 0220.002 6293.082 6430.171 2760.010 9−0.009 9
    w−0.001 3720.000 9354.963 777−0.413 9470.003 5−0.006 5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-01-05
  • 修回日期:  2021-04-15
  • 网络出版日期:  2021-05-19
  • 刊出日期:  2021-08-25

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