Localization and de-noising seismic signals on SASW measurement by wavelet transform

SASW method is a nondestructive in situ testing method that is used to determine the dynamic properties of soil sites and pavement systems. Phase information and dispersion characteristics of a wave propagating through these systems have a significant role in the processing of recorded data. Inversion of the dispersive phase data provides information on the variation of shear-wave velocity with depth. However, in the case of sanded residual soil, it is not easy to produce the reliable phase spectrum curve. Due to natural noises and other human intervention in surface wave date generation deal with to reliable phase spectrum curve for sanded residual soil turn into the complex issue for geological scientist. In this paper, a time–frequency analysis based on complex Gaussian Derivative wavelet was applied to detect and localize all the events that are not identifiable by conventional signal processing methods. Then, the performance of discrete wavelet transform (DWT) in noise reduction of these recorded seismic signals was evaluated. Furthermore, in particular the influence of the decomposition level choice was investigated on efficiency of this process. This method is developed by various wavelet thresholding techniques which provide many options for controllable de-noising at each level of signal decomposition. Also, it obviates the need for high computation time compare with continuous wavelet transform. According to the results, the proposed method is powerful to visualize the interested spectrum range of seismic signals and to de-noise at low level decomposition.     

Determination of dispersive phase velocities for SASW method using harmonic wavelet transform

The spectral analysis of surface waves (SASW) method is an in situ, seismic method for determining the shear wave velocity (or maximum shear modulus) profile of a site. The SASW test consists of three steps: field testing, evaluation of dispersion curve by phase unwrapping method, and determination of shear modulus profile by inversion process. In general, field testing and dispersion curve evaluation are regarded as simple work. However, because of characteristic of Fourier transform used in the conventional phase unwrapping method, dispersion curve is sensitive to background noise and body waves in the low frequency range. Furthermore, under some field conditions such as pavement site, the usual phase unwrapping method can lead to erroneous dispersion curve. To overcome problem of the usual phase unwrapping method, in this paper, a new method of determining dispersion curve for SASW method was applied using time–frequency analysis based on harmonic wavelet transform as an alternative method of a current phase unwrapping method. To estimate the applicability of proposed method to SASW method, numerical simulations at various layered soil and pavement profiles were performed and the dispersion curves by proposed method are more reliable than those by the usual phase unwrapping method.     

基于EMD的信号瞬时特征的小波分析方法

提出了一种基于EMD(Empirical Mode Decomposition)的信号瞬时特征的小波分析方法。用这种方法提取非平稳信号的瞬时频率和瞬时幅值分三个基本步骤：首先,用EMD把信号分解成IMF(Intrinsic Mode Function)分量;接着,对IMF分量进行小波分析,从小波系数的幅角函数中提取小波脊线;最后,从小波脊线中提取瞬时频率和瞬时幅值。通过对仿真信号的分析,验证了该方法能有效地分析非平稳信号。     

利用小波变换提取面波群速度

选取了几种常见的小波母函数,分别提取了同一理论下的面波数据的群速度,并与理论群速度进行对比,结果表明Morlet小波提取面波群速度的效果最好.此外,将Morlet小波与常用的多重滤波提取群速度的结果进行了比较,结果表明: ① 多重滤波法非常依赖高斯滤波系数α的取值,α的取值应随面波周期的增大而减小;② 在α取值得当的前提下,在20—35 s周期范围内多重滤波法提取面波群速度的相对误差比Morlet小波小,在周期大于35 s时,两者相对误差相近; ③ 合适的α值的选取需在不同周期段耗费大量时间进行大量试验,这说明多重滤波法不具备自适应性;而采用小波变换分析短周期信号时,时间窗变窄,频率窗变长,当分析长周期信号时,时间窗变长,频率窗变窄,具有对信号的自适应性,这是小波变换相比多重滤波法的最大优点.      

基于小波包变换的重叠地震波分离

利用dmey小波包变换提取时频特征,采用阈值熵搜索最优小波包基方法,用Matlab语言编程,实现对重叠地震信号的分离.通过对2009年2月20日柯坪M5.2地震波和其后ML4.7余震的研究,发现用该方法能清楚地找到同源地震重叠的位置,为地震定位提供准确、可靠的震相识别依据,从而提高地震定位的精度.     

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。     

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.     

利用相关域小波变换进行SWD资料预处理

随钻地震(SWD)的波场十分复杂,对钻头有效信号和地表机械干扰成分的分析是SWD重要的资料预处理步骤.本文利用有效信号和噪声带有周期性或时延差异等时间结构特征,引入相关域小波变换进行SWD信号检测和分析.有效信号在钻柱内往复多次传播,因而带有严格的周期性,泥浆泵等机械发出的噪声也是周期性的,这些成分在自相关域内可以得到很好的凸显.SWD波场的各种成分,由于到达各个接收道的时延不同,在互相关域的特定时延处也能够得到凸显.利用小波变换对这些在相关域内得到凸显的成分进行多分辨分析,能够获得优势频率范围、周期、衰减等主要特征.根据这些信息,设计出合理的SWD处理方法,初步得到了有效信号的直达波.数据试处理结果表明,相关域小波变换是随钻地震的一个有效的预处理方法.     

基于拉东变换的瑞雷面波频散分析与应用

为克服面波谱分析法(SASW)提取频散曲线抗干扰能力差、不能得到多模式频散曲线等缺点,对拉东变换法进行了改进,不对原始记录进行数字处理,避免了数字处理效应的影响.通过对信号的频谱分析、结合场地的地质条件,选择频散分析的频率、速度范围,来达到规避高视速度的直达波、反射波.理论模型合成记录和实际资料的处理表明:研究的频散分析方法是有效的且适应性强,取得了较好的效果.     

Modelling evapotranspiration using discrete wavelet transform and neural networks

This study combines wavelet transforms and feed‐forward neural network methods for reference evapotranspiration estimation. The climatic data (air temperature, solar radiation, wind speed, relative humidity) from two stations in the United States was evaluated for estimating models. For wavelet and neural network (WNN) model, the input data was decomposed into wavelet sub‐time series by wavelet transformation. Later, the new series (reconstructed series) are produced by adding the available wavelet components and these reconstructed series are used as the input of the WNN model. This phase is pre‐processing of raw data and the main different of the WNN model. The performance of the WNN model was compared with classical neural networks approach [artificial neural network (ANN)], multi‐linear regression and Hargreaves empirical method. This study shows that the wavelet transforms and neural network methods could be applied successfully for evapotranspiration modelling from climatic data. Copyright © 2009 John Wiley & Sons, Ltd.     

电性源时域地空电磁数据小波去噪方法研究

基于飞艇的时间域地空电磁探测系统,具有勘探深度大、效率高、空间分辨率高、飞行控制容易等优势.但在低空飞行测量过程中,飞艇飞行高度、航迹、姿态等受风向、大气气流、地形、地面局部温度场变化等影响而发生变化,导致固定在艇囊前端的接收线圈发生运动,切割大地磁场,产生了电磁噪声、运动噪声、基线漂移等,从而影响电磁数据的电阻率成像质量.因此,研究地空电磁信号中多种噪声的去除方法,对数据的反演解释非常重要.由于地空电磁信号中有效信号频带与部分噪声频带相重叠,使用传统滤波或消噪方法具有一定局限性.因此,本文提出一种综合小波去噪法:根据地空电磁信号的特点,采用sym8小波基;基于小波多分辨率分析原理,利用小波高尺度近似分量估计基线漂移,以校正电磁数据中的基线;基于小波阈值收缩原理,采用5层小波分解、极小极大阈值配合硬收缩函数的消噪方法,来压制数据中的其余噪声.最后,通过异常环模型的理论响应和实测数据进行算法的验证,结果表明这种综合消噪法对多种噪声均有很好的抑制作用,是一种实用有效的时间域地空电磁数据消噪方法.     

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.     

压缩小波变换和非线性阈值技术压制磁共振尖峰噪声方法研究

地面磁共振是一种新的地球物理探测方法,能够通过探测地下水中氢质子丰度获取地下水含量、孔隙度等水文地质信息.然而,磁共振信号甚为微弱,仅达到纳伏级（10^-9 V）,极易受到噪声干扰.其中,尖峰噪声对磁共振信号影响最为严重,亟待研究有效的噪声抑制方法.小波多尺度分解硬阈值是近两年国际磁共振领域专家提出的尖峰噪声有效消除方法,但硬阈值算法设定阈值的固有缺陷会引发信号震荡,出现伪吉布斯效应,导致信号损失.基于此,本文提出压缩小波变换（Synchrosqueezing Wavelet Transform,SWT）和非线性阈值处理（Nonlinear Thresholding,NT）算法联合消除磁共振信号尖峰噪声干扰.首先选择Morlet小波作为基小波,使得信号与噪声数据具有更高的时频集中性,利于尖峰噪声消除.其次,基于压缩小波系数进行非线性处理,可以弥补利用硬阈值和软阈值进行噪声消除时所引起的信号损失.仿真数据和实际数据结果表明,SWT联合NT方法可以利用单次采集数据有效消除尖峰噪声干扰并还原信号.本文提出的消噪方法将为磁共振数据后续反演解释,如多指数弛豫反演,奠定坚实的基础.

     

Evaluation of density in layer compaction using SASW method

SASW test, which is non-intrusive and rapid in the field application, was used to evaluate the layer density in the roller compaction without performing the complicated inversion process. The concept of normalized shear wave velocity was introduced to minimize the effect of confinement in the density evaluation. SASW test was performed to determine the shear wave velocity of the layer, and the free–free resonant column (FF–RC) test was adopted to determine the correlation between the normalized shear wave velocity and density of the site, which is almost unique independent of confinement. Testing and data reduction procedures of both tests were briefly discussed and an evaluation procedure of the field density was proposed by effectively combining in-situ shear wave velocity determined by the SASW test with the correlation between the normalized shear wave velocity and the density determined by the FF–RC test. Finally, the feasibility of the proposed method was verified by performing a field case study at Hoengsung road construction site. Field densities determined by the proposed method matched well with those determined by sand cone tests, showing the potential of applying the proposed method in the field density evaluation.     

加权抛物Radon变换叠前地震数据重建

基于部分动校正（NMO）后反射同相轴在CMP道集上的抛物线走时近似,给出了加权抛物Radon变换叠前地震数据重建方法（WPRT）. WPRT通过在迭代过程中引入变化着的权系数,拓展和改进了传统抛物Radon变换方法,使其可同时完成不规则采样的规则化和空道及近偏移距道重建,且有更高的计算效率. 文中给出了应用WPRT进行近偏移距和中偏移距的空地震道重建及数据规则化的算法实现. 理论模型和实际地震资料的地震数据重建结果显示了本文算法的优点.     

基于小波变换的地震反应分析

从小波变换的基本原理出发，提出了地震地面运动的小波模型，以具有时频特性的小波基来表示，并用于多自由度的地震反应分析，导出了相应的公式，实例表明，这种方法是可行的。     

Stream flow characterization and feature detection using a discrete wavelet transform

An exploration of the wavelet transform as applied to daily river discharge records demonstrates its strong potential for quantifying stream flow variability. Both periodic and non-periodic features are detected equally, and their locations in time preserved. Wavelet scalograms often reveal structures that are obscure in raw discharge data. Integration of transform magnitude vectors over time yields wavelet spectra that reflect the characteristic time-scales of a river's flow, which in turn are controlled by the hydroclimatic regime. For example, snowmelt rivers in Colorado possess maximum wavelet spectral energy at time-scales on the order of 4 months owing to sustained high summer flows; Hawaiian streams display high energies at time-scales of a few days, reflecting the domination of brief rainstorm events. Wavelet spectral analyses of daily discharge records for 91 rivers in the US and on tropical islands indicate that this is a simple and robust way to characterize stream flow variability. Wavelet spectral shape is controlled by the distribution of event time-scales, which in turn reflects the timing, variability and often the mechanism of water delivery to the river. Five hydroclimatic regions, listed here in order of decreasing seasonality and increasing pulsatory nature, are described from the wavelet spectral analysis: (a) western snowmelt, (b) north-eastern snowmelt, (c) mid-central humid, (d) south-western arid and (e) ‘rainstorm island’. Spectral shape is qualitatively diagnostic for three of these regions. While more work is needed to establish the use of wavelets for hydrograph analysis, our results suggest that river flows may be effectively classified into distinct hydroclimatic categories using this approach. © 1998 John Wiley & Sons, Ltd.     

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…     

Non-linear filter, using the wavelet transform, applied to seismological records

As any process in Nature, seismic recordsare affected by noise that the analystwould want to eliminate. One of the mostcommon techniques used to minimise thisnoise effect is the application of linearfilters, which reduce the bandwidth of thesignal. This method is based on the FourierTransform, and therefore any perturbationon the coefficients affects the entirerecord.We have developed a non-linear filter basedon the multiresolution analysis of theDiscrete Time Wavelet Transform (DTWT). Themain idea is to use the time-frequencylocalisation properties of the waveletdecomposition. Each coefficient isassociated to a window on thetime-frequency plane, so any perturbationwould only affect the time and frequencyrange of the correspondent window.The procedure we propose has three stages:periodic noise elimination, spikesreduction and, finally, the non-linearfiltering. The non-linear filter acts bythresholding the wavelet coefficients. Thethresholding estimator will depend on thesignal-noise ratio (SNR) in each of thefrequency bands associated to the waveletdecomposition.We have compared the proposed method to thecoherent structures method (Mallat, 1998)and to two 4^th order linear filterbanks (Butterworth and Elliptic filters),applying all of them to a syntheticdatabase, and a real earthquake databaserecorded by the Short Period ROA Network.The proposed method improves the SNR in the87% of the tested events, being therelative rms error less than three, and themaximum amplitude relative error less than10% in the 90% of the synthetic database.