Multi-Time Scale Analysis of Runoff at the Yangtze Estuary Based on the Morlet Wavelet Transform Method

Runoff series of the Yangtze River presents an intricate variation tendency under the reinforced influence of human activities.The Morlet Wavelet Transform method has been applied to analyze the annual runoff data from 1950 to 2011 at the Yangtze River Estuary.It can clearly reveal the multi-time scales structure,break point,change and distribution of periodic variation in the different time scales of the runoff series.The main conclusions are that：1） Repeated periodic oscillations accompanied by an extremely large fluctuation are presented in the runoff series with an obvious difference between wet and dry years,and the major periods of the time series are about 3,8,16 and 23 years respectively.Among them,the presented maximum periodic oscillation is 23 years scale.2） In the 23-year time scale,the wet periods are 1950-1958,1969-1980 and 1992-2003,and the dry periods are 1959-1968,1981-1991 and 2004-2011.3） It can be predicted from the view of long time scales that the low annual runoff will likely occur in the near future.     

小波二叉分解在信号压缩处理中的应用

讨论了小波在信号压缩中由正交离散小波变换缺乏平移不变性，会对信号压缩的性能造成不利的影响。为了解决这一问题，在讨论正交离散小波变换时对每个变换尺度上均对偶采样和奇采样的奇采样的结构进行保留，从而衍生出了二叉树结构，按相应准则可确最优分解路线并 此可重构原信号，最后给出的仿具结果证实了该变换方式较正交离散小波变换在取得较高压缩能力的同时，具有更高的信噪比。     

a尺度正交多小波的构造

证明了由任意a尺度正交单尺度函数和任意一组正交滤波器生成a尺度r重正交多尺度函数的一个充分必要条件,并给出了a尺度r重正交多小波的一种构造方法.     

临夏台钻孔应变观测资料浅析

对临夏台YRY-4型钻孔应变的观测数据进行初步分析,认为其观测资料稳定、可靠,观测质量好。同时,利用小波变换分析2015-01-01~2019-10-31的观测数据,提取甘肃及周边区域发生的M5.0以上且临夏台震中距在300 km左右的3次地震前的异常信息,发现异常信息在细节的第7阶更明显和集中。     

张家界水环境演变与旅游发展关系

通过实地监测并全面分析张家界国家森林公园水环境演变趋势，发现金鞭溪水质下降最为明显，而且随着游客的逐年增多，呈现出加速恶化的趋势。金鞭溪水质恶化主要表现为蓝藻，绿藻迅速繁殖，感官质量下降，主要原因是上游接待区锣鼓塔排出的污水中总磷超标，通过建立基于环境脆弱因子的动态阈值模型，计算得出：在不超出张家界景区最为脆弱的环境因子－金鞭溪水质标准：总磷≤0．02前提下，金鞭溪上游接待区住宿设施生态阈值，春季临界床位数1186，夏季为3057，冬季为545，秋季为333。     

鲜水河带和松潘,龙门山带可能发生M〉5级地震的模式识别的验算与改进

1985年作者曾完成“鲜水河地震带和松潘、龙门山地震带未来半年内是否可能发生5级以上地震的模式识别”的研究。本文首先使用当时选定的九个原始特征、示性特征判别阈值和投票阈值,将模式识别方法用于近几年对这两个地震带的预报实践表明,按识别结果作出的预报是正确的。本文在原来基础上又增添一个新的原始特征——“断层面总面积∑E(2/3)”,重新求证最佳判别阈值和投票阈值,最后得到了更加稳定的识别模型。     

Wavelet analysis and nonparametric test for climate change in Tarim River Basin of Xinjiang during 1959–2006

Using wavelet analysis, regression analysis and the Mann-Kendall test, this paper analyzed time-series (1959–2006) weather data from 23 meteorological stations in an attempt to characterize the climate change in the Tarim River Basin of Xinjiang Uygur Autonomous Region, China. Major findings are as follows: 1) In the 48-year study period, average annual temperature, annual precipitation and average annual relative humidity all presented nonlinear trends. 2) At the 16-year time scale, all three climate indices unanimously showed a rather flat before 1964 and a detectable pickup thereafter. At the 8-year time scale, an S-shaped nonlinear and uprising trend was revealed with slight fluctuations in the entire process for all three indices. Incidentally, they all showed similar pattern of a slight increase before 1980 and a noticeable up-swing afterwards. The 4-year time scale provided a highly fluctuating pattern of periodical oscillations and spiral increases. 3) Average annual relative humidity presented a negative correlation with average annual temperature and a positive correlation with annual precipitation at each time scale, which revealed a close dynamic relationship among them at the confidence level of 0.001. 4) The Mann-Kendall test at the 0.05 confidence level demonstrated that the climate warming trend, as represented by the rising average annual temperature, was remarkable, but the climate wetting trend, as indicated by the rising annual precipitation and average annual relative humidity, was not obvious.     

基于小波与包络线的土壤有机质高光谱估测

以陕西省横山县为研究区,对小波分析法与包络线去除法融合应用于土壤有机质高光谱估测的有效性进行了探究。利用陕西省横山县84个土壤样本的光谱反射率和有机质含量数据,采用小波分析法去除了部分噪声,通过光谱一阶微分变换,提高光谱反射率与有机质的相关性,采用包络线去除法提取建模因子,减少数据冗余后最终采用回归分析法建立土壤有机质的估测模型。结果表明,与未采用小波分析法建立的模型比较,建模因子的相关性提高了0.1~0.25,模型的判定系数可达0.932,提高了0.207,检验样本的均方根误差RMSE为0.107,减小了0.116。总体而言,小波分析与包络线去除法联合应用于土壤有机质高光谱估测是有效的且所建模型精度较高,但数据质量对模型精度有重要影响。