基于改进变分模态分解的北极海域声速剖面分类*

应用支持向量机对北极声速剖面进行分类,特征量提取是关键。该文采用一种基于经验模态分解的改进变分模态分解算法,以准确提取声速剖面特征量。算法首先对声速剖面信号进行经验模态分解,依据最大类间方差原则划分各分量边际谱主频带,以相似度作为最小分解层数判断标准,获得最小分解层数,进行变分模态分解。对北极区海水声速实测数据(信号)处理表明,该方法可有效提取信号经验模态分解各分量的希尔伯特边际谱特征,进行支持向量机分类,实现对北极海域声速剖面的分类识别,解决以往人工分类耗时久的问题。

Sound speed profile classification of Arctic sea area based on improved VMD

Using support vector machine to classify the North Pole sound velocity profile, feature extraction is the key. In this paper, an improved variational mode decomposition algorithm (IEVMD) based on empirical mode decomposition is used to extract the sound velocity profile features accurately. The algorithm firstly conducts empirical mode decomposition for the sound velocity profile signal, divides the main frequency band of the marginal spectrum of each component according to the principle of maximum intercategory variance, takes similarity as the judgment standard of the minimum decomposition layer number, obtains the minimum decomposition layer number, and performs the variational mode decomposition. The processing of measured acoustic velocity data (signals) shows that this method can effectively extract the Hilbert marginal spectrum characteristics of the empirical mode decomposition of signals, carry out support vector machine classification, realize the classification and identification of acoustic velocity profiles in the Arctic sea area, and solve the problem of time-consuming artificial classification in the past.