Separation of obliquely incident and reflected irregular waves by the Morlet wavelet transform

An existing 2D time-domain method for separating irregular incident and reflected waves by wavelet transform [Ma et al., 2010. A new method for separation of 2D incident and reflected waves by the Morlet wavelet transform. Coastal Eng., 57(6):597–603] is extended to account for obliquely incident irregular waves propagating over sloping bottoms. The linear shoaling and refraction coefficients are adopted to determine the amplitude and phase changes of waves. The optimal central frequency of the Morlet wavelet is determined by the minimum Shannon wavelet entropy. Numerical tests show that the present method can accurately separate waves over horizontal depths. For waves at sloping bottoms, however, the separation errors increase as bottom slope increases and are significant for waves with incident angle larger than π/3.     

基于小波变换和HIS变换的海冰SAR与光学遥感影像融合方法研究

Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral information, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be improved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transformation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar(ASAR) loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensity-saturation(HIS) space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue(RGB) space, and the optical image from the China-Brazil Earth Resources Satellite(CBERS-02B) was transferred from the RGB space to the HIS space. The principal component analysis(PCA) method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.     

Numerical modelling of deformation of multi-directional random waves over a varying topography

Two numerical formulations of the breaking phenomenon were implemented in a numerical model for random wave propagation based on the elliptic formulation of the mild-slope equation. The randomness of the wave field was simulated based on a spectral component method, in which the 3-D spectrum is discretised in components of equal energy. One of the breaking process formulations is based on the concept of breaking each independent spectral component. The other is based on the distribution of the local amount of energy dissipated through the independent spectral components. The model based on the concept of breaking each independent spectral component produces the best estimates of the wave field, when the numerical results are compared with laboratory data.     

基于二维连续小波变换的颗粒物数字粒径方法研究

对于海岸工程的研究而言,测定泥沙、砾石、卵石等颗粒物的中值粒径具有重要的研究意义。近年来,许多学者逐渐将统计学方法应用于分析泥沙等颗粒物的粒径大小及其级配分布,小波变换是该研究领域时新的统计计算数学方法之一。由于一维连续小波变换(1D-CWT)在估算颗粒数值粒径方面存在一定的局限性,本研究基于二维连续小波变换(2D-CWT),提出了新的颗粒物粒径统计学分析方法,用于估算筛分良好的颗粒物中值粒径。以玻璃珠、卵石块和砾石三类不同性质的颗粒物构建数字粒径实验分析样本,对新方法进行了测试和验证。研究表明基于2D-CWT的数字粒径分析方法能够快速、便捷地估算出筛分良好的颗粒物的中值粒径,且计算结果准确、稳定。相较于基于1D-CWT的数字粒径分析方法,2D-CWT方法在颗粒物数字粒径分析方面有着更高的计算成功率和更广的适用性。     

海岛礁光学和雷达影像基于改进非子采样小波变换的自动配准

Homologous feature point extraction is a key problem in the optical and synthetic aperture radar （SAR） image registration for islands. A new feature point extraction method using a threshold shrink operator and non-subsampled wavelet transform （TSO-NSWT） for optical and SAR image registration was proposed. Moreover, the matching for this proposed feature was different from the traditional feature matching strategies and was performed using a similarity measure computed from neighborhood circles in low-frequency bands. Then, a number of reliably matched couples with even distributions were obtained, which assured the accuracy of the registration. Application of the proposed algorithm to SPOT-5 （multi-spectral） and YG-1 （SAR） images showed that a large number of accurately matched couples could be identified. Additionally, both of the root mean square error （RMSE） patterns of the registration parameters computed based on the TSO-NSWT algorithm and traditional NSWT algorithm were analyzed and compared, which further demonstrated the effectiveness of the proposed algorithm. The algorithm can supply the crucial step for island image registration and island recognition.     

A new method for separation of 2D incident and reflected waves by the Morlet wavelet transform

A new method based on the Morlet wavelet transform for separating a 2D wave field into incident and reflected waves is proposed in this paper. The principle of this method, first, is derived for constant depths. Then, using the linear shoaling theory, the method is extended to an arbitrary sloping bathymetry. Owing to the time-frequency characteristic of wavelet transform, the present method can separate waves in the real time domain and is not confined by the stationary assumption of waves. The efficiency and accuracy of this method are demonstrated using numerical simulated data.     

The initialization of nonlinear water waves in a numerical wave flume

This study investigates the initialization of nonlinear free-surface simulations in a numerical wave flume.Due to the mismatch between the linear input wavemaker motion and the kinematics of fully nonlinear waves,direct numerical simulations of progressive waves,generated by a sinusoidally moving wavemaker,are prone to suffering from high-frequency wave instability unless the flow is given sufficient time to adjust.A time ramp is superimposed on the wavemaker motion at the start that allows nonlinear free-surface simulations to be initialized with linear input.The duration of the ramp is adjusted to test its efficiency for short waves and long waves.Numerical results show that the time ramp scheme is effiective to stabilize the wave instability at the start of the simulation in a wave flume.     

小波变换在HT-7Tokamak磁流体动力学振荡动态频谱分析中的应用

将Gauss复小波变换方法成功地应用于HT-7Tokamak磁流体动力学振荡动态频谱分析中.研究结果表明,这种方法具有较好的时间分辨率和空间分辨率,比较适用做动态频谱分析.对典型放电数据的分析结果表明, m=1模的振荡频率与等离子体压强梯度有着密切的关系.     

光滑表面内孤立波光学遥感图像的实验研究

基于光学遥感图像的内孤立波参数反演是一项重要的工作。本文在实验室条件下提出一种新方法用于模拟光滑表面情况下内孤立波的光学遥感成像。基于二维内波水槽、LED平板面光源和CCD相机搭建仿真光学遥感系统探测内孤立波。水平表面的光学遥感图像和垂向内孤立波传播图像被同时探测,旨在探讨在光滑表面下,光学遥感与内孤立波的响应。结果表明,内孤立波传播经过时,CCD1相机获得暗纹,暗纹的特性随光源入射角的变化而变化。光学遥感特征参数和垂向波要素相对应。实验还显示光学遥感图像的暗纹宽度与内孤立波的半波宽度在不同内孤立波振幅下呈现正相关关系。该系统有着现象清晰,重复性高的优点,为定量研究光学遥感成像机理提供科学依据。     

基于小波变换的水沙关系特征分析:以长江大通站为例

根据长江大通站近50年(1957～1999年)的水沙数据,利用复Morlet小波变换,分析水沙时间序列多尺度小波特征,建立了不同时间尺度上的水沙关系式.分析结果显示:(1) 大通站水沙序列年内洪、枯季周期波动最为显著,流量的长周期特征明显,含沙量与输沙量的短周期特征显著.(2) 大通站水沙序列多尺度特征明显,流量的振荡周期主要为3～6 a和8～16 a;含沙量多尺度振荡周期较为平稳,4～6 a的周期振荡中心尺度呈明显下降的趋势;输沙量以4～6 a和10～14 a的周期显著振荡,输沙量变化平稳,较含沙量变化有一定的滞后期.(3) 根据流量小波特征分析,近50年的水沙变化过程大致可分为3个时期,分时段拟合水沙关系曲线,发现总体水沙曲线的指数与正常期和丰水期的接近,而与少水期的存在较大的差距.少水期系数较小、指数较大,表明在少水期泥沙的供给量较少,但水流的搬运能力增强.     

变水深对畸形波及其时频能量谱的影响

使用VOF (volume of fluid)方法实现了变化水深条件下畸形波的数值模拟,使用小波分析方法计算模拟结果的时频能量谱,发现变化水深可以加强波浪的非线性相互作用,使转化到高频端的能量更多,产生了不对称程度更大的畸形波.     

基于小波变换和LSTM神经网络的格陵兰岛近海海域海平面变化预测

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。