HHT的滤波特性及在声波测井波列信号处理中的应用(英文)

阵列声波信号是典型的非线性、非平稳信号,Hilbert～Huang变换（HHT）是处理非平稳信号的一种比较新的时频分析方法。通过对信号进行经验模态分解（EMD）和对瞬时频率的求解,可以获得声波信号的时一频谱。其关键技术就是进行经验模态分解,任何非平稳的信号都可以分解为有限数目并且具有一定物理意义的固有模态函数。EMD方法可以理解为以声波信号极值特征尺度为度量的时频滤波过程。滤波器充分保留了声波信号本身的非线性和非平稳特征,在声波信号的滤波和去噪中具有很大的优势。文中介绍了HHT时频滤波的实现过程,并列举了一些声波测井波列实例,说明了该方法的有效性。     

希尔伯特-黄变换地震信号时频分析与属性提取

地震信号属于非线性和非平稳信号,传统的分析方法主要包括短时傅立叶变换、小波变换和Cohen类时频分布等等;希尔伯特-黄变换是分析非平稳信号的新方法,该方法的关键部分是信号的经验模态分解,通过经验模态分解,复杂的信号可以分解为有限的数量很少的几个固有模态函数,从而可以得到信号的希尔伯特时频谱;将该方法应用于单个的地震道数据,可以对地震道进行经验模态分解并得到希尔伯特谱,应用于地震剖面,可以得到意义更加明确的瞬时频率和瞬时振幅等地震属性,模型试算和实际应用表明了该方法的有效性.     

用小波变换进行信号特征检测的原理与方法

在前人工作的基础上,我们运用卷积来定义一个连续信号的二进小波变换。在考虑小波函数的相位特征基础上给出了其重现公式。另外,本文论述了这种小波变换定义能直接用于信号特征的检测,并给出了可用于信号奇异性分析的小波函数和尺度函数。     

时程信号的Hilbert-Huang变换与小波分析/

在简要介绍时程信号的小波分析和Hilbert-Huang变换(HHT)理论的基础上,通过地震波和其它时程信号实例,对比分析了小波变换和HHT变换结果. 比较显示：HHT变换和小波变换均能用于对非平稳的信号进行分析,并能捕捉到信号变化的主要特征;与受所选母波影响较大的小波分析不同,HHT变换得到的固有模态函数是直接从原始时程数据中分离出来的,它更能反映原始数据的固有特性;小波分析得到的谱的能量在频率范围内分布较广,而HHT变换的Hilbert谱的大部分能量都集中在一定的时间和频率范围内,能清晰地刻画信号能量随时间、频率的分布. 因此,Hilbert-Huang变换不仅是对非平稳信号进行分析的有效方法,而且也是检测时程信号局部特征的有用工具.     

Seismic signal analysis based on the dual-tree complex wavelet packet transform

We tried to apply the dual-tree complex wavelet packet transform in seismic signal analysis. The complex wavelet packet transform (CWPT) combine the merits of real wavelet packet transform with that of complex continuous wavelet transform (CCWT). It can not only pick up the phase information of signal, but also produce better “focalizing” function if it matches the phase spectrum of signals analyzed. We here described the dual-tree CWPT algorithm, and gave the examples of simulation and actual seismic signals analysis. As shown by our results, the dual-tree CWPT is a very effective method in analyzing seismic signals with non-linear phase. Foundation item: Cultural Heritage Protection Program of State Administration of Cultural Heritage (200001).     

基于小波变换的结构地震响应与能量计算分析

多分辨分析可将地震信号分解到不同的频段。本文推导了运用多分辨分析计算多自由度体系地震响应的基本公式,讨论了各频段地震信号及结构响应的能量分配。最后,通过多自由度体系的算例验证了弹性体系在原始地震作用下的动力响应可以由该地震作用在时域的各小波分量的动力响应叠加而得,同时将高频抑制后的重构信号应用于近似模型的计算,并利用能量分析明确了地震信号与结构响应在各频段的能量分配。     

基于曲率模态和小波变换的结构损伤位置识别

小波变换具有在时域和频域内表征信号局部特性的能力,能够在不同尺度下对结构响应中的突变信号进行放大和识别.在结构曲率模态基础上,本文提出了一种基于小波变换的结构损伤检测和定位方法.利用双正交小波函数对损伤前后结构的曲率模态进行小波变换,用损伤前后小波变换系数残差建立了结构损伤指标,通过小波变换系数残差的分布统计情况判定损伤的存在并确定其位置.应用简支梁数值模拟结果对该方法进行了验证.     

联合小波变换与偏振分析自动拾取微地震P波到时

对微地震P波到时的自动拾取是微地震信号分析和数据处理的主要目标之一。基于小波变换的多尺度分析思想,对微地震信号进行小波处理后的小波系数代替原始信号,应用包含在小波变换系数中的信号偏振信息,提出了联合小波变换与偏振分析自动拾取微地震信号P波到时的方法。通过对嘉阳煤矿监测的实际微地震数据进行小波变换,用多尺度小波分解的各个尺度单支重构信号构成协方差矩阵,求解不同尺度协方差矩阵的最大特征值和次大特征值求取P波到时定位函数,实现P波到时的自动拾取,取得了满意的结果.     

基于小波变换与小波包变换的降噪方法比较

在模拟地震记录信号中加入信噪比为17的高斯白噪声,然后分别采用小波降噪和小波包降噪方法,对含噪信号进行降噪处理。在不同降噪阈值下,比较降噪后信号的信噪比。结果表明:在同一降噪阈值下,小波包降噪后信号的信噪比高于小波降噪后信号的信噪比,而且采用wbmpen方法给定的阈值明显可以提高降噪后信号的信噪比。     

Assessment of drought trend and variability in India using wavelet transform

ABSTRACT

This paper presents an analysis of trends in six drought variables at 566 stations across India over the period 1901–2002. Six drought variables were computed using standardized precipitation index (SPI). The Mann-Kendall (MK) trend test and Sen’s slope estimator were used for trend analysis of drought variables. Discrete wavelet transform (DWT) was used to identify the dominant periodic components in trends, whereas the significance of periodic components was examined using continuous wavelet transform (CWT) based global wavelet spectrum (GWS). Our results show an increasing trend in droughts in eastern, northeastern and extreme southern regions, and a decreasing trend in the northern and southern regions of the country. The periodic component influencing the trend was 2–4 years in south, 4–8 years in west, east and northeast, 8–64 years in central parts and 32–128 years in the north; however, most of the periodic components were not statistically significant.     

小波变换在地电场数据分析中的应用

随着城镇化发展的加快, 上海的地电场观测受到来自周围环境噪声的影响日益严重, 这些噪声干扰对于地震前兆异常的分析判断带来很多不确定性. 本文主要探讨通过小波变换方法来分解地电场观测原始信号, 分析环境噪声对原始观测数据的影响强度. 利用异常信号的小波模值对比分析了滤波结果, 并通过绘制极化方位图检验了该方法在实际运用中的效果. 结果表明： 数字滤波方法能很好地去除日常干扰所带来的背景噪声, 对于重构的地电场信号, 也能较好地反映其原始信号变化特征; 滤波后的自然电场异常信号保留了原始信号中主要的变化特征, 并能反映其原始变化规律; 重构的地电场信号能够突出信号中异常信号极化方位角, 使极化方位收敛有利于实际运用.     

位场小波变换研究进展

小波变换的具有多尺度分析的特点,在位场资料分析中得到了广泛应用.本文总结了位场小波变换的理论基础及应用现状,介绍了位场小波基函数的概念.小波分析可以应用于位场分离、去噪、反演及综合地质解释等分析.文中从小波的数学性质和物理意义等角度讨论了位场资料处理中小波基函数的选取问题.     

小波分析在地球物理及大地测量中的应用

小波分析是重要的时—领域分析工具，在地球物理和大地测量相关问题研究中逐渐显示出其独特的作用。本文较全面地介绍了现阶段小波分析在重力异常分解、固体潮、地球自转变化、ENSO和重力场展开等地球物理及大地到量领域的研究进展，并在此基础上对地球物理及大地测量领域小波分析的应用前景做了进一步分析。     

River flow forecasting using wavelet and cross‐wavelet transform models

In this study, short‐term river flood forecasting models based on wavelet and cross‐wavelet constituent components were developed and evaluated for forecasting daily stream flows with lead times equal to 1, 3, and 7 days. These wavelet and cross‐wavelet models were compared with artificial neural network models and simple perseverance models. This was done using data from the Skrwa Prawa River watershed in Poland. Numerical analysis was performed on daily maximum stream flow data from the Parzen station and on meteorological data from the Plock weather station in Poland. Data from 1951 to 1979 was used to train the models while data from 1980 to 1983 was used to test the models. The study showed that forecasting models based on wavelet and cross‐wavelet constituent components can be used with great accuracy as a stand‐alone forecasting method for 1 and 3 days lead time river flood forecasting, assuming that there are no significant trends in the amplitude for the same Julian day year‐to‐year, and that there is a relatively stable phase shift between the flow and meteorological time series. It was also shown that forecasting models based on wavelet and cross‐wavelet constituent components for forecasting river floods are not accurate for longer lead time forecasting such as 7 days, with the artificial neural network models providing more accurate results. Copyright © 2008 John Wiley & Sons, Ltd.     

Identification method of weak seismic phases on the basis of wavelet packet transform

This paper describes a sort of new method identifying seismic phase by the name of wavelet packet transform. Perfectness and development of the wavelet packet transform is based upon the idea of its multiscale analysis. The method of wavelet packet transform can depict the anomalous changes information of transient spectra of seismic wave onset, and come true the target of identifying seismic phase especially weak seismic phase. Then this paper presents discriminating examples of simulating digital signals and actual seismic phase. Compared with conventional seismic phase discrimination, studied results show that the wavelet packet transform method is an available tool of weak signal analyses, and have unexampled merits and attractive application foreground. This research gets hold of Higher School Doctor Scientific Research Foundation (No. 157 WJ0704 9435611) and supported by the National Natural Science Foundation (No. 49474211).     

Identification method of seismic phase in three-component seismograms on the basis of wavelet transform

IntroductionThe three-component seismograms represents ground motion. Seismic signal contains various types of phase arrivals. Some of the most significant are the P, S, LQ and LR arrivals. Accurately determining the time of these arrivals 'is importal to determine the location of an event.Although a lot of work in determining the location have been done, researchers have not carriedout the target in trying to determine this information more accurately from single-station seismogram due to …     

用小波分析提取重力固体潮

地球固体潮观测对验证各种地球模型、研究地球内部构造具有重要意义,可为地震预报提供重要的参考依据.BBVS-120甚宽带地震计具有较低的自噪声水平,在低频端输出信号的频率很低,涵盖固体潮信息频带范围.本文提出:BBVS-120地震计输出信号中存在周日波、半日波和1/3日波的固体潮信号,利用小波分析,提取垂直分量中的重力固体潮信号,并作调和分析.     

Seismic data compression based on integer wavelet transform

Introduction With the development of the seismological observation technique and deep-going of seismicdata application fields, especially the digitization of data in earthquake station networks, theimprovement of the precision, the data quantity increases as geometric order, which bringdifficulty to saving and transfering these data. To keep all information, seismic data, like medicalimages, should be compressed without error in many applications. In generally, traditionalcompression meth…     

Seismic data compression based on integer wavelet transform

Due to the particularity of the seismic data, they must be treated by lossless compression algorithm in some cases. In the paper, based on the integer wavelet transform, the lossless compression algorithm is studied. Comparing with the traditional algorithm, it can better improve the compression rate. CDF (2, n) biorthogonal wavelet family can lead to better compression ratio than other CDF family, SWE and CRF, which is owe to its capability in canceling data redundancies and focusing data characteristics. CDF (2, n) family is suitable as the wavelet function of the lossless compression seismic data. Contribution No.04FE1019, Institute of Geophysics, China Earthquake Administration.