共查询到19条相似文献，搜索用时 140 毫秒
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本文选取1989-2021年我国台风风暴潮直接经济损失统计数据，依据线性趋势法和Mann-Kendall非参数检验法进行分析，结果表明，32年间我国风暴潮灾害经济损失呈现显著下降趋势，整体呈厚尾分布特征，采用对数化处理后呈显著的正态分布特征。采用Morlet小波变换对我国台风风暴潮直接经济损失的周期变化规律进行分析，t检验结果显示，全域存在准两次高频振荡，1～2年及7～8年的周期振荡，但随时间变化年际周期逐渐缩短为3～5年，说明风暴潮经济损失序列存在高频振荡和多周期嵌套的低频振荡规律。在此基础上，采用Daubechies小波分解分离高频信号和低频信号，均方根误差和信噪比精度分析结果表明，当小波基设置消失矩为7，分解层数为2时，我国台风风暴潮直接经济损失时间序列具有最优分解重构效果。对各分解层进行小波系数平稳性检验和白噪声检验，建立的小波分解-ARMA组合模型的模拟精度和预测精度均优于传统的自回归移动平均模型和Fourier级数拓展模型，证明了小波分解法在我国台风风暴潮经济损失快速评估中具有可靠性和优越性。 相似文献
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为了对锚泊状态下未来0.5 h的船面风速和风向进行估算,提出了一种基于小波变换和最小二乘支持向量机的估算方法。首先对原始风速数据进行正交分解,得到X轴和Y轴风速;然后分别对X轴和Y轴风速进行小波分解,提取出低频和高频数据序列;其次利用最小二乘向量机方法,分别对低频、高频序列进行估算,分别将X轴和Y轴各序列估算结果进行叠加得到X轴和Y轴估算风速;最后利用矢量法则,同时实现了风速和风向未来0.5 h的估算。以试验船在东海海域锚泊状态下船舶气象仪所测的风速风向数据进行建模与实例验证分析,结果表明该方法具有较高的估算精度。 相似文献
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利用1992年10月~2002年7月的TOPEX/POSEIDON(T/P)卫星高度计月平均格点数据分析了热带大西洋(15°S~25°N,5°~50°W)海面高度距平的低频变化。由热带大西洋大约10 a海面高度距平变化的标准差分析得到:在赤道附近海区(2°~5°N,25°~45°W)、非洲沿岸海区(11°~16°N,16°~18°W)海面高度波动剧烈。对海面高度距平进行经验正交函数(EOF)分析,得到EOF的3个模态分别占有方差比例为51.5%,13.2%和7.9%。第一模态揭示的是热带辐合带(ITCZ)的季节性迁移导致海面高度距平沿着ITCZ平均位置经向倾斜的1 a周期变化,第一模态还显示了太阳辐射的季节差异引起南北两个海盆海面高度的整体升降。第二模态描述了中心分别位于(3°N,40°W)和(7°N,45°W)附近两个涡漩的变化。第三模态表征的是几内亚海湾上升流和赤道北部下降流在6~7月强度达到最大。对EOF时间系数曲线的经验模态分解(EMD),结果表明热带大西洋低频变化包含的成分主要有:0.5,1,2,4和6 a。其中1 a周期是热带大西洋海面高度变化最主要的周期成分,0.5 a周期和2 a周期也是热带大西洋海面高度变化的重要形式;而4 a和6 a周期所占的比例较小。另外EMD方法还分解出1997~1998年太平洋El Nino事件对热带大西洋海面高度的影响。 相似文献
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侧扫声呐回波信号是形成侧扫声呐图像的基础,是侧扫声呐系统对水下目标的最直接观测量, 将一维小波变换与非线性增强方法相结合,提出了一种基于小波变换的侧扫声呐回波信号非线性增强算法, 用以改善侧扫声呐图像对比度低、噪声强度大的问题。首先利用改进的 Bayes 阈值对侧扫声呐 ping 信号进行一维小波分解,提取信号特征信息;然后利用 2 种不同的非线性函数对高、低频小波系数进行处理;最后利用小波反变换重构信号,形成增强后的侧扫声呐图像。实测数据验证结果表明：利用该算法对侧扫声呐 ping 信号进行处理,实现了侧扫声呐图像对比度的增强和对噪声的抑制,可以获取较好的图像视觉效果。 相似文献
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《中国海洋大学学报(自然科学版)》2021,(12)
水下极低频电磁信号探测在深远海探测领域应用前景广阔,然而极低频电磁信号易受海流运动感应电磁噪声影响,为了拓展极低频电磁探测在海洋中的应用,需要通过信号处理的方式压制噪声,提高信号信噪比。小波分析具有多分辨率分析及时频局部化的优势,适于处理海水运动感应电磁噪声等非平稳信号,本文将海流感应电磁场特征引入小波分析中,基于正演模拟及小波系数分析确定阈值的小波阈值去噪方法压制噪声,利用合成的包含大地电磁信号、海流感应电磁噪声的极低频信号进行实验,并利用南黄海海域电磁信号探测试验数据进行验证。研究结果表明,噪声压制后的极低频信号信噪比明显提高,信号在时频域识别能力得到改善。 相似文献
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南海海面高度变化及其与太平洋上涛动信号的关系 总被引：1，自引：1，他引：0
本文使用循环平稳经验正交函数(CSEOF)方法分析了南海海面高度(SCS-SSH)的时空变化模态,并对它们与太平洋海盆尺度振荡的关系进行了探讨分析。结果表明,SCS-SSH的第一个CSEOF模态是季节变化模态,其变化强度受到一个与厄尔尼诺-南方涛动(ENSO)有关的低频信号的调制,即在厄尔尼诺期间季节变化的幅度减弱(最大可降低30%,1997/98)而在拉尼娜期间季节变化增强。SCS-SSH的第二个CSEOF模态是年际-年代际尺度的低频变化模态,其空间模态的月与月之间的差异微弱,而时间模态和太平洋年代际振荡(PDO)指数高度相关。然后,我们使用独立成分分析(ICA)方法提取了太平洋中的五个主要振荡成分,并检验了它们对SCS-SSH变化的各自影响。分析表明,纯粹的ENSO模态(类似于太平洋东部型ENSO)对SCS-SSH的低频变化的影响比较微弱,而ENSO的红化模态(类似于太平洋中部型ENSO)对SCS-SSH的低频变化具有明显影响。由于ENSO的红化模态是PDO信号的一个主要成分,这一结果解释了为什么在影响SCS-SSH的低频变化上PDO比ENSO更重要。径向鞍型振荡模态、黑潮延伸体处的增温模态、以及赤道的降温模态也由ICA方法提取出来,但它们对SCS-SSH低频变异的影响微弱。进一步的分析表明,太平洋的涛动信号可能以不同的方式来影响南海海面高度变化和海表温度变化。 相似文献
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海平面变化包含多种不同时间尺度信息，传统的预测方法仅对海平面变化趋势项、周期项进行拟合，难以利用海平面变化的不同时间尺度信号，使得预测精度不高。本文基于深度学习的预测模型，提出一种融合小波变换（wavelet transform，WT）与LSTM （long short-term memory，LSTM）神经网络的海平面异常组合预测模型。首先利用小波分解得到反映海平面变化总体趋势的低频分量和刻画主要细节信息的高频分量；然后通过LSTM神经网络对代表不同时间尺度的各个分量预测和重构，实现海平面变化的非线性预测。基于该模型的海平面变化预测的均方根误差、平均绝对误差和相关系数分别为12.76 mm、9.94 mm和0.937，预测精度均优于LSTM和EEMD-LSTM预测模型，WT-LSTM组合模型对区域海平面变化预测具有较好的应用价值。 相似文献
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In this paper, we propose a hybrid forecasting model to improve the forecasting accuracy for depth-averaged current velocities (DACVs) of underwater gliders. The hybrid model is based on a discrete wavelet transform (DWT), a deep belief network (DBN), and a least squares support vector machine (LSSVM). The original DACV series are first decomposed into several high- and one low-frequency subseries by DWT. Then, DBN is used for high-frequency component forecasting, and the LSSVM model is adopted for low-frequency subseries. The effectiveness of the proposed model is verified by two groups of DACV data from sea trials in the South China Sea. Based on four general error criteria, the forecast performance of the proposed model is demonstrated. The comparison models include some well-recognized single models and some related hybrid models. The performance of the proposed model outperformed those of the other methods indicated above. 相似文献
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Eight years of sea surface height data derived from the TOPEX/Poseidon altimeter, are analyzed in order to identify long- and a-periodic behavior of the North Atlantic sea level. For easy interpolation, sea surface height data are converted into sea surface topography data using the geoid derived from EGM96 to degree 360. Principal Component Analysis is used to identify the most dominant spatial and temporal variations. In order to separate dominant periodic signals, a yearly and a half-yearly oscillation, as well as alias effects from imperfect ocean tide corrections, are estimated independently by a Harmonic Analysis and subtracted. The residuals are smoothed by a 90-day moving average filter and examined once again by a PCA, which identifies a low-frequency variation with a period of approximately 6-7 years and an amplitude of about 1 dm, as well as a large sea level change of partially more than ±1 dm within only few months. This sea level change can also be seen in yearly and seasonal sea level residuals. Furthermore, the analysis shows a significant sea level change in 1998 occurring almost over the whole North Atlantic, which is not clearly identified by the PCA. Similar results are obtained by analyzing sea surface temperature and sea level pressure data. 相似文献
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Eight years of sea surface height data derived from the TOPEX/Poseidon altimeter, are analyzed in order to identify long- and a-periodic behavior of the North Atlantic sea level. For easy interpolation, sea surface height data are converted into sea surface topography data using the geoid derived from EGM96 to degree 360. Principal Component Analysis is used to identify the most dominant spatial and temporal variations. In order to separate dominant periodic signals, a yearly and a half-yearly oscillation, as well as alias effects from imperfect ocean tide corrections, are estimated independently by a Harmonic Analysis and subtracted. The residuals are smoothed by a 90-day moving average filter and examined once again by a PCA, which identifies a low-frequency variation with a period of approximately 6–7 years and an amplitude of about 1 dm, as well as a large sea level change of partially more than ±1 dm within only few months. This sea level change can also be seen in yearly and seasonal sea level residuals. Furthermore, the analysis shows a significant sea level change in 1998 occurring almost over the whole North Atlantic, which is not clearly identified by the PCA. Similar results are obtained by analyzing sea surface temperature and sea level pressure data. 相似文献
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An empirical orthogonal function analysis has been applied to solving the forecast problem of the monthly mean sea surface temperature for the East China Sea and the adjacent waters. The data matrix of the original sea surface temperature fields can be separated into two components, /'. e. the spatial and the temporal components. According to the properties of its spatial component that almost does not change with time and through the extrapolation of its temporal component, the prediction for large area sea surface temperature will be achieved. The time coefficients for temporal component are predicted by means of traverse and vertical time series method.On the basis of forecasting for these two years, it has been proved that the method objectively reflected the internal relations and interactions of sea surface temperature among the stations of water area. The results of the suggested method are better than the predicted method for a collection of each individual stations. The mean absolute error of p 相似文献
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基于分位数回归的西南太平洋阿根廷滑柔鱼栖息地模型研究 总被引：6，自引：0，他引：6
阿根廷滑柔鱼是西南大西洋重要头足类资源,研究其变动、渔场分布与海洋环境的关系是其可持续利用的基础。本文利用分位数回归方法对表温(5m)、表层盐度(5m)、57m盐度及其盐度差、海面高度、叶绿素与阿根廷滑柔鱼钓获率进行回归分析,在中位数和高位数2种情况下分别建立阿根廷滑柔鱼的栖息地指数(HSI)模型,从而揭示西南大西洋阿根廷滑柔鱼栖息地的分布模式。研究表明,本文建立的各分位数回归方程均能较好地解释自变量与应变量的关系(P0.05)。1~5月在60°W以西、42°S~53°S阿根廷沿海的大部分海域,其HIS值基本上在0.7以上;而58°W以东海域的HIS在0.4以下。阿根廷滑柔鱼适宜栖息地分布(HIS大于0.6)有明显的季节变化。 相似文献
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Mehmet Özger 《Ocean Engineering》2010,37(16):1443-1451
Wave heights and periods are the significant inputs for coastal and ocean engineering applications. These applications may require to obtain information about the sea conditions in advance. This study aims to propose a forecasting scheme that enables to make forecasts up to 48 h lead time. The combination of wavelet and fuzzy logic approaches was employed as a forecasting methodology. Wavelet technique was used to separate time series into its spectral bands. Subsequently, these spectral bands were estimated individually by fuzzy logic approach. This combination of techniques is called wavelet fuzzy logic (WFL) approach. In addition to WFL method, fuzzy logic (FL), artificial neural networks (ANN), and autoregressive moving average (ARMA) methods were employed to the same data set for comparison purposes. It is seen that WFL outperforms those methods in all cases. The superiority of the WFL in model performances becomes very clear especially in higher lead times such as 48 h. Significant wave height and average wave period series obtained from buoys located off west coast of US were used to train and test the proposed models. 相似文献
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基于TOPEX/Poseidon资料的南海海面高度场的时空特征分析 总被引：1，自引：0，他引：1
采用经验正交函数(EOF)分解方法，对TOPEX／Poseidon卫星高度计在南海获得的1992年10月到1999年9月约7a的海面高度观测资料进行分析，从而获得南海海面高度距平场典型的空间分布型态及其对应的时间变化特征。结果表明，南海海面高度距平场在空间上主要表现为两种典型的分布结构：(1)由于冬、夏季风反转造成海盆尺度的涡旋结构，这种分布结构对南海海面高度距平场的方差贡献达27．46％；(2)NE—SW即吕宋—越南反相双涡结构，其方差贡献达20．37％。这两个模态都明显反映了季风的反转以及季风结构所造成的影响。同时，对各空间典型场所对应的时间系数序列进行了傅立叶谱分析，结果表明南海海面高度距平场存在多种时间尺度的变化。 相似文献