Signal reconstruction of surface waves on SASW measurement using Gaussian Derivative wavelet transform

Surface wave method consists of measurement and processing of the dispersive Rayleigh waves recorded from two or more vertical transducers. The dispersive phase data are inverted and the shear wave velocity versus depth is obtained. However, in case of residual soil, the reliable phase spectrum curve is difficult to be produced. Noises from nature and other human-made sources disturb the generated surface wave data. In this paper, a continuous wavelet transform based on mother wavelet of Gaussian Derivative was used to analyze seismic waves in different frequency and time. Time-frequency wavelet spectrum was employed to localize the interested seismic response spectrum of generated surface waves. It can also distinguish the fundamental mode of the surface wave from the higher modes of reflected body waves. The results presented in this paper showed that the wavelet analysis is able to determine reliable surface wave spectrum of sandy clayey residual soil.     

S变换谱分解技术在深反射地震弱信号提取中的应用

在深反射地震资料处理中,当来自深部的有效弱信号和噪声干扰频带差异较小且难以区分时,传统滤波方法的应用会受到限制.谱分解方法是一种使用离散傅里叶变换,基于信号的频率-振幅谱等信息生成高分辨率地震图像的方法,通常用来识别介质物性横向分布特征,处理复杂介质内频谱变化和局部相位的不稳定性等问题,包括定位复杂断层和小尺度断裂等.S变换作为一种新的时频分析方法,具有自动调节分辨率的能力,近些年来被广泛应用到勘探地震、大地电磁等数据处理中,逐渐成为地球物理方法中噪声压制的有效方法之一.与常规石油反射地震资料相比,深反射主动源地震为了探测深部结构信息,常采用大药量激发方式、长排列观测系统等,导致深部有效信号基本湮灭在噪声干扰之中.针对深反射数据特点,本文结合谱分解和S变换技术,首先设计了简单的脉冲函数实验数据,证实S变换方法的有效性,同时说明谱分解方法的效果受所用时频分析方法影响较大,而其中决定分辨能力的变换窗函数的选取尤为重要.在此基础上,分别应用到深反射地震资料的单道和叠加剖面实际数据上,对比分析了传统变换谱分解和S变换谱分解的应用效果,单道资料对比结果表明:相比传统谱分解,S变换谱分解方法具有自动调节分辨率的能力,能够精确的标定深反射地震资料中弱信号不同时刻的频率分量;叠加剖面资料应用结果表明:由S变换谱分解得到的剖面结果与其他谱分解方法结果整体上具有较高的一致性,同时清晰地刻画出原叠加剖面上被噪声湮灭的低频细节特征,提高了剖面的分辨率及同相轴连续性;对比结果明显看出,Gabor变换谱分解方法得到的结果同相轴较为破碎,分析原因认为这是由Gabor变换的时频分解方法的定长窗函数所致,窗口大小不会随着信号频率的变化来调节长度,只能在处理的过程中根据一定的记录长度范围选取窗函数参数,而S变换谱分解方法在窗函数的选取时,通过时变信号的局部频率特征自动调节窗口长度,能够更好的刻画各个频段的细节特征,在深反射剖面成像应用中效果尤为明显.本文结果表明S变换谱分解技术在深地震叠加剖面上的应用有效地提高了来自深部弱反射信号的信噪比和分辨率,并刻画出了叠加剖面上所不具有的低频细节特征,在实际深反射地震资料处理中能有效保护低频弱信号获得更好的成像效果.本文为深地震反射资料中弱信号的保护处理找到一种有效的方法.     

Weak Seismic Signal Extraction Based on the Curvelet Transform

Seismic signal denoising is a key step in seismic data processing. Airgun signals are easy to be interfered with by noise when it travels a long distance due to the weak energy of active source signal of the airgun. Aiming to solve this problem, and considering that the conventional Curvelet transform threshold processing method does not use the seismic spectrum information, we independently process the Curvelet scale layer corresponding to valid data based on the characteristics of the Curvelet transform of multi-scale, multi-direction and capable of expressing the sparse seismic signals in order to fully excavate the information features. Combined with the Curvelet adaptive threshold denoising the algorithm, we apply the Curvelet transform to denoising seismic signals while retaining the weak information in the signal as much as possible. The simulation experiments show that the improved threshold denoising method based on Curvelet transform is superior to the frequency domain filtering, wavelet denoising and traditional Curvelet denoising method in detailed information extraction and signal denoising of low SNR signals. The calculation accuracy of the relative wave velocity variation of underground medium is improved.     

Method for Identifying Micro-seismic <Emphasis Type="Italic">P</Emphasis>-Arrival by Time-frequency Analysis Using Intrinsic Time-Scale Decomposition

A method to identify the P-arrival of microseismic signals is proposed in this work, based on the algorithm of intrinsic timescale decomposition (ITD). Using the results of ITD decomposition of observed data, information of instantaneous amplitude and frequency can be determined. The improved ratio function of short-time average over long-time average and the information of instantaneous frequency are applied to the time-frequency-energy denoised signal for picking the P-arrival of the microseismic signal. We compared the proposed method with the wavelet transform method based on the denoised signal resulting from the best basis wavelet packet transform and the single-scale reconstruction of the wavelet transform. The comparison results showed that the new method is more effective and reliable for identifying P-arrivals of microseismic signals.     

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).     

基于物理小波的频谱分解方法及应用研究

在地震资料频谱分解中,采用匹配地震子波的物理小波,依据地震信号的特征,用振幅、能量衰减率、能量延迟时间及地震子波的中心频率等四类参数构造基本小波,把地震信号分解在小波域,高频分量能够得到精细的刻画.本文以物理小波变换为工具, 给出了该变换中的核函数的选择方法,进而提出了基于物理小波变换的频谱成像方法.我们将此方法用于海上某油田河流相储层的描述,并与常规软件中的小波变换频谱成像结果进行了对比, 结果表明,本文提出的方法更能精细地刻画地质事件.     

Hilbert-Huang 变换与大地电磁噪声压制

大地电磁信号具有非线性、非平稳、非最小相位特征,不符合以Fourier变换为基础的传统功率谱估计的基本要求. Hilbert-Huang变换是近年发展起来的处理非线性、非平稳信号的完全局部时频分析方法. 本文在简要介绍Hilbert-Huang变换基本原理与算法基础上,以实际数据分析为例,探讨了它在大地电磁信号处理及噪声压制中的应用. 提出利用Hilbert时-频能量谱对大地电磁信号进行时段筛选,以提高信号品质,增强数据处理的质量和资料的可解释性. 利用经验模态分解方法及其多尺度滤波特征,可以有效地分析MT信号中的噪声分布特征,并进行干扰压制.     

利用小波变换分析朝鲜核爆波形特征

基于小波变换方法,对2009-2017年黑龙江省牡丹江地震台（MDJ）记录的5次朝鲜核爆信号进行Daubechies小波8层多尺度分解,通过对比分析近似和细节部分信息,发现核爆信号进行小波分解后,在不同层存在相同特征,且有别于地震信号分解后在相应各层的特征。可见,利用该方法可有效识别朝鲜核爆。     

基于小波分频叠前相干噪声压制方法

基于小波分析的去噪方法在地震资料叠前处理中得到了广泛应用.本文主要介绍利用小波变换的分频特性来压制相干噪声.通过小波分频技术将叠前地震信号分解为不同频带,然后利用有效波和相干干扰波的频谱差异来区分有效信号和噪声,最后利用加权方法去掉不需要的噪声信息来达到去除相干噪声的目的.实际资料的处理结果表明：基于小波分频方法能很好地压制相干噪声,从而提高地震资料信噪比和分辨率.     

Dyadic wavelet analysis of PDA signals

The dyadic wavelet transform is used to analyze PDA measured signals in order to identify the CASE-damping factor, which may be directly calculated from the dyadic wavelet analysis, not from the correlation study; accordingly, the pile capacity may be more exactly estimated by the CASE method. The dyadic wavelet transform can decompose a PDA measured signal into an incident impact wave and a reflected impulse wave at the certain scale that are clearly shown on the wavelet transform graph. The relation between the incidence and the reflection has been established by a transfer function based on the dyadic wavelet transform and the one-dimensional wave equation, whose phase is the time delay between the incident and the reflected and whose magnitude is a function of the CASE-damping factor. An autocorrelation function analysis method is proposed to determine the time delay and to estimate the magnitude of the transfer function that is determined by the ratio of the maximum of the autocorrelation function to the second peak value represented the reflected wave on the autocorrelation function graph. Thus, the damping factor is finally determined. An analog signal, a PIT signal and five PDA signals demonstrate the proposed methods, by which the time delay, the CASE-damping factor, and pile capacity are determined. The damping factors and pile capacity are good agreement with those by CAPWAP.     

基于稀疏约束反演谱分解的频变AVO储层识别方法

时频分析技术是描述和分析非平稳信号的有效工具,如何提高谱分解的时-频分辨率受到了国内外学者的广泛关注.基于稀疏约束反演的谱分解方法(ISD)是近些年提出的一种具有高时-频分辨率的谱分解方法.然而传统ISD方法采用复雷克子波作为母小波函数,很大程度限制了信号分析的分辨率和精度.本文创新性地将传统ISD方法拓展到其他线性变换当中,提高了ISD方法的适用性.利用合成数据测试并对比了传统方法与ISD方法的时频分析结果,研究表明ISD方法不但能提供高分辨率的振幅谱,还能提供可靠的时-频相位谱信息.在此基础上,本文提出将高时-频分辨率的ISD方法与频变AVO(FAVO)反演相结合,得到能指示流体的地震波频散属性.将该方法应用于实际资料,结果表明基于ISD时频方法的FAVO反演结果具有更高的分辨率和准确度,对于储层油气具有更好的指示作用.     

希尔伯特—黄变换在地电阻率数据处理中的应用

希尔伯特—黄变换用于处理非线性非平稳信号,由经验模态分解和希尔伯特谱分析2部分组成。本文采用希尔伯特—黄变换方法,相继对大同地震台地电阻率月值数据和宝昌地震台地电阻率月值、整点值数据进行处理。结果显示:(1)大同、宝昌地震台地电阻率月值数据对应的Hilbert谱具有较高分辨率,高幅值在归一化频率0.05—0.15区间内呈"余弦"变化形态;(2)希尔伯特—黄变换在提取地电阻率异常变化、高频信息及去除噪声等方面效果较好,在未来地电资料处理中具有广泛的应用前景。     

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.     

用小波变换能量线性度方法识别天然地震与爆破或塌方

为了提取天然地震和爆破或塌方记录波形在震源深度、震源尺度、震源破裂机制、地震波传播途径、地震波衰减等方面的差异特征信息,本对山东数字化台网记录的天然地震和爆破或塌方波形进行了小波多分辨率的能量线性度特征分析,提出了用小波变换能量线性度方法识别天然地震与爆破或塌方事件．结果表明：在精细结构小波分解信号“能量”线性度方面,天然地震主要集中在-2．0～1．0之间,爆破或塌方主要集中在2．0～3．4之间;在精细结构小波分解信号“能量”最大值对应的小波分解尺度方面,爆破或塌方主要集中在4～5,频段集中在0．7～3．1Hz之间,而天然地震主要集中在1～2,频段集中在6．25～25Hz之间．     

小波基在GPS-IR反演土壤湿度中优选方法的研究

全球定位系统多径干涉遥感技术(Global Positioning System-Interference Reflection,GPS-IR)是一种基于卫星反射信号的新遥感技术,可实现土壤湿度、雪深、海平面等方面的监测.针对卫星反射信号分离问题,本文利用小波分析对信噪比(Signal-to-Noise Ratio,SNR)中的卫星反射信号进行分离.小波基的选择直接影响到卫星反射信号分离的效果,本文通过对比低阶多项式、sym10、bior6.8、dmey和coif5这5种方法的实际消趋和反演效果来优选.经实验表明:利用coif5小波基分解6层能够有效提高卫星反射信号的分离精度和信号的分辨率,获取的相对延迟相位与土壤湿度之间的线性关系显著增强.     

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).     

汶川MS8.0级地震前后ULF电磁辐射频谱特征研究

本文基于电磁波频谱理论研究方法,对2008年四川汶川M_S8.0地震前后金河、剑阁及郑州二砂三个电磁波台站的观测资料进行FFT和小波变换分析,研究了电磁辐射数据快速傅里叶频谱变化特征和在不同尺度下小波变化的分解,发现在汶川地震前确实有异常信息存在.结果表明:(1) FFT动态谱图像说明,地震前电磁波频谱变化特征较明显,在时间、频段上均显示了阶段性进程特征,且随着震中距的增大,辐射能量越小,异常出现的时间越晚;(2) 小波分解显示了地震前电磁波异常信号低频部分出现的时间较早;距震中较近的台站,异常信息在高频部分相对明显;距震中稍远的台站,异常信息在低频部分相对明显.     

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.     

A method for absorption compensation based on adaptive molecular decomposition

In this paper, we present a new method for seismic stratigraphic absorption compensation based on the adaptive molecular decomposition. Using this method, we can remove most of the effects resulting from wavelets truncation and interference which usually exist in the common time-frequency absorption compensation method. Based on the assumption that the amplitude spectrum of the source wavelet is smooth, we first construct a set of adaptive Gabor frames based on the time-variant properties of the seismic signal to transform the signal into the time-frequency domain and then extract the slowly varying component (the wavelet’s time-varying amplitude spectrum) in each window in the time-frequency domain. Then we invert the absorption compensation filter parameters with an objective function defined using the correlation coefficients in each window to get the corresponding compensation filters. Finally, we use these filters to compensate the time-frequency spectrum in each window and then transform the time-frequency spectrum to the time domain to obtain the absorption-compensated signal. By using adaptive molecular decomposition, this method can adapt to isolated and overlapped seismic signals from the complex layers in the inhomogeneous viscoelastic medium. The viability of the method is verified by synthetic and real data sets.