多阈值自适应滤波在科研鱼探仪噪音处理中的应用

Application of multi-threshold adaptive filtering in noise processing of fishfinder

  • 摘要: 在联合地球物理探测任务中,科研鱼探仪数据中广泛存在的条带状强干扰噪声,严重影响了海底羽状流等构造体识别的准确性。为提升剖面信噪比与构造信息保真度,本文提出一种基于多阈值自适应滤波方法,融合目标强度、走向方向与梯度变化等空间域特征,构建三重阈值判定体系,并引入滑动窗口自适应中值滤波机制,实现条带状噪声压制,增强海底冷泉羽状流的边界清晰度与内部结构连续性,通过模型验证了算法实用性,并在实测冷泉数据处理中取得良好效果,为后续构造识别与定量分析提供了可靠的前处理基础。

     

    Abstract: In joint geophysical exploration mission, strong and banded interference noise in the fish finder data for research are is common, which could seriously affect the accuracy of the identification of seafloor plumes and the like. To improve the signal-to-noise ratio of the profile and the fidelity of the water structural information, we proposed a multi-threshold adaptive filtering method that integrates the spatial features of target strength, orientation, and gradient change, constructed a triple threshold determination system, and introduced a sliding window adaptive median filtering mechanism, with which the banded noise can be suppressed, the boundary clarity of the seafloor cold-spring plume and the continuity of the internal structure be enhanced, The practicality of the method was verified by modelling, and good results were achieved against the measured cold spring data. This study provided a reliable pre-processing basis for the subsequent identification of water body structure and quantitative analysis.

     

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