峨眉山近55a来水资源变化的多时间尺度分析

利用峨眉山1951-2005年逐月气温和降水观测资料,利用高桥浩一郎公式计算出地表蒸发量及水资源量.分析了峨眉山近55a来降水量、蒸发量及水资源的气候特征和变化趋势,并利用墨西哥帽子小波变换分析了水资源的时间-频率的多层次时间尺度变化特征,揭示了在不同时间尺度下峨眉山水资源序列变化的周期和突变点,并根据主周期对未来水资源变化趋势进行了预测.     

基于广义模型约束的时间域航空电磁反演研究

由于航空电磁具有海量数据,因此快速有效的成像和反演手段至关重要.本文针对层状介质模型推导与实现了广义模型约束条件下时间域航空电磁一维反演.从正则化反演的目标函数出发,通过改变模型约束项构造Lp范数反演和聚焦反演,进而通过改变模型求解域构造出基于小波变换的稀疏约束反演.针对不同反演方法目标函数的构建方式,本文进一步从数学原理上分析不同反演方法的预期效果,并通过理论模型和实测数据进行验证.结果表明L0.8范数反演、聚焦反演和基于小波变换的稀疏约束反演可以得到更符合地下层状介质陡变界面的反演结果.

     

我国松花江流域和辽河流域降水的长期变化规律和未来趋势分析

利用1881～2002年我国东北地区的月、季降水资料,采用历史曲线分析、功率谱分析、小波变换等方法,重点分析了松花江流域和辽河流域降水的长期变化规律。结果表明:松花江流域和辽河流域的降水都存在着明显的年代际变化,松花江流域降水以27～30年周期为主,辽河流域降水周期比松花江流域略长,大概为35～38年左右。根据降水自身演变规律及自回归方法预测的结果,估计未来5～10年,松花江流域仍将处于少雨期,辽河流域少雨期维持时间可能会稍长一些。     

隧道围岩断面轮廓分维数的小波分析算法及应用

隧道的超欠挖对衬砌结构稳定性影响较大,研究其规律,对于掌握围岩受力,确保施工安全,具有深远意义。隧道断面轮廓超欠挖序列具有统计自相似性,根据小波分析的多分辨率的特点,本文给出了利用小波理论估计分维数的方法。选定恰当的小波函数和分解层数,对隧道围岩断面的整体轮廓、拱顶和边墙的超欠挖序列分别进分形维数的小波分析估算,结果安全可靠。根据计算结果,分析了设计围岩类别与断面轮廓曲线分形维数估计值之间的关系,并结合现场实际情况,简明分析了断面轮廓分形维数与围岩稳定性之间的关系。     

长江流域月降水的时空变化及其与AO/NAO的时滞相关分析

运用旋转经验正交函数(REOF)和交叉小波变换方法,分析了长江流域1960—2012年月降水的时空分布特征及其与AO/NAO的时滞相关关系。结果表明:(1)长江流域月降水异常分布的局域特征明显,可划分为4个主要的空间异常型:两湖平原型、东南丘陵型、西南型及北方型;(2) 4个主要的空间异常模态的累计贡献率达45. 8%,其中REOF1(18. 9%)的正异常区位于巫山及两湖平原地区;东南丘陵一带为REOF2(12. 3%)的正值中心,REOF3(8. 9%)突出了云贵高原北部和四川盆地南部的正异常区; REOF4(5. 7%)的负值中心处于四川盆地北部及其以北地区。这4个模态的时间系数(RPCk)的变化幅度较大,且其位相均呈不规则的正负交替,表明各降水异常区具有复杂的时间演变特征;(3) 4个主要异常模态与AO、NAO在特定时间尺度皆存在显著相关的现象,巫山及两湖平原地区、四川盆地北部及其以北地区的月降水均对AO表现出及时响应;东南丘陵一带的月降水对AO、NAO的最佳滞后响应时间都较长。     

陕西省2021年极端秋雨成因分析

利用NCEP/NCAR2.5°×2.5°的逐月再分析资料和NOAA提供的逐月海温资料等从海温异常、大尺度环流异常、水汽输送异常等角度分析了2021年秋季陕西降水异常偏多的原因,结果表明：（1）小波交叉谱分析显示Ni?o3.4区海温距平与陕西秋季降水存在2～4年的共振周期,前期冬季海温与次年秋季降水关系呈正相关的周期共振。（2）受双峰型La Ni?a事件影响,2021年9-11月副热带高压（下文简称“副高”）控制的范围明显偏大,副高脊线位置较气候态偏北1度,西伸脊点较气候态偏西23度,造成主雨带偏西偏北。（3）中低层西北太平洋到我国华北北部的异常反气旋,与孟加拉湾北侧异常的气旋中心,造成气流在我国西北地区东部异常辐合。（4）从水汽收支来看,陕西2021年秋季水汽净流入较气候态偏多66.7%,尤其是东边界的异常水汽输送,还增强了低层冷垫,利于暖湿气流在冷垫上爬升导致陕西降水较常年异常偏多。     

A novel method for enhancement of radiometric resolution using image fusion

The Resourcesat-I satellite is equipped with different types of sensors with varied characteristics. For the effective utilization of the available multi-sensor, multi-temporal, multi-spectral and multi-radiometric data from these sensors, fusion of digital image data has become a valuable technique. Image fusion enhances the information content and helps in better discrimination of various land cover types. The Resourcesat-1 has equipped with three sensors, AWiFS, LISS-III and LISS-IV, which are having identical spectral resolutions, with different spatial, radiometric and temporal resolutions. The spatial resolutions ratio of the data set for merging are required to be maximum of 1:6, where as the data sets (AWiFS and LISS-III) that are used in the current study are having the ratio of 1:2.5 approximately. A novel merging technique is designed, which retains the multi-spectral response of the input data in the output data. The merged data set provides the higher spatial and radiometric resolutions. In order to evaluate the fusion merits quantitatively, all the data sets are digitally classified and studied the output classes for homogeneity and clear discrimination. A comprehensive comparative study is carried out between the fused image and the LISS-III image based on the contingency matrix and the scatter plots, which demonstrates the strength of fused image for discriminating the object classes at 23.5 m spatial and 10-bit radiometric resolutions. The merged data set gives the improved classification accuracy.     

MT时间序列的小波去噪分析

从本质上说 ,MT时间序列中噪声的强度与类型是能否取得MT响应参数无偏估计的决定性因素。当MT时间序列中磁场和电场中都含有相关噪声时 ,传统的去噪方法已无能为力。结合小波分析与MT时间序列的特征 ,提出了一种基于小波分析的MT时间序列去噪方法 ,讨论了基于小波分析的噪声识别 ,分析了理论数据通过小波分解与重构实现的去噪处理 ,探讨了对实测时间序列的固定源和随机干扰的去噪处理     

Prediction of extreme wave heights using neuro wavelet technique

Precise prediction of extreme wave heights is still an evading problem whether it is done using physics based modeling or by extensively used data driven technique of Artificial Neural Network (ANN). In the present paper, Neuro Wavelet Technique (NWT) is used specifically to explore the possibility of prediction of extreme events for five major hurricanes Katrina 2005, Dean 2007, Gustav 2008, Ike 2008, Irene 2011 at four locations (NDBC wave buoys stations)¹ namely; 42040, 42039, 41004, 41041 in the Gulf of Mexico. Neuro Wavelet Technique is employed by combining Discrete Wavelet Transform and Artificial Neural Networks. Discrete wavelet transform analyzes frequency of signal with respect to time at different scales. It decomposes time series into low (approximate) and high (detail) frequency components. The decomposition of approximate components (extreme events in the ocean wave series) can be carried out up to the desired multiple levels in order to provide relatively smooth varying amplitude series. This feature of wavelet transforms make it plausible for predicting extreme events with a better accuracy. In the present study third, fifth and seventh level of decompositions are used which facilitates 3 to 7 times filtering of low frequency events and seems to pay the dividend in the form of better prediction accuracy at extreme events. To develop these Neuro wavelet models to forecast the waves with lead times of 12 hr to 36 hr in advance, previously measured significant wave heights at same locations were used. The results were judged by wave plots, scatter plots and other error measures. From the results it can be concluded that the Neuro Wavelet Technique can be employed to solve the ever eluding problem of accurate forecasting of the extreme events.     

Using wavelet transforms to analyze nonlinear ship rolling and heave-roll coupling

The use of wavelet transforms is explored to investigate the nonlinear dynamical characteristics of ship roll and coupled heave-roll motion. The harmonic character, double period character and chaotic character are observed via a time–frequency window of the wavelet transform. Typical wave parameters in different stability regions are considered. Features such as restoring rolling, divergence rolling, steady state and chaotic responses of ship roll are obtained as well. The investigation in this paper not only highlights the feasibility of using wavelet transforms in the analysis of nonlinear dynamic characteristics of ship rolling in waves, but also shows how it could enhance the analysis abilities.