Phase spectrum estimation of the seismic wavelet based on a criterion function

Phase spectrum estimation of the seismic wavelet is an important issue in high-resolution seismic data processing and interpretation. On the basis of two patterns of constant-phase rotation and root transform for wavelet phase spectrum variation, we introduce six sparse criteria, including Lu’s improved kurtosis criterion, the parsimony criterion, exponential transform criterion, Sech criterion, Cauchy criterion, and the modified Cauchy criterion, to phase spectrum estimation of the seismic wavelet, obtaining an equivalent effect to the kurtosis criterion. Through numerical experiments, we find that when the reflectivity is not a sparse sequence, the estimated phase spectrum of the seismic wavelet based on the criterion function will deviate from the true value. In order to eliminate the influence of non-sparse reflectivity series in a single trace, we apply the method to the multi-trace seismogram, improving the accuracy of seismic wavelet phase spectrum estimation.

Measurement of interstation phase velocity by wavelet transformation

In this paper, we present wavelet transformation method to measure interstation phase velocity. We use Morlet wavelet function as mother wavelet to filter two seismograms at various period of interest, and correlate the wavelet filtered seismograms to form cross-correlogram. If both wavelet filtered signals are in phase at that period, the phase of the cross-correlogram is a minimum. Using 3-spline interpolation to transform cross-correlation matrix to a phase velocity verse period image, it is convenient for us to measure interstation phase velocity.

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

This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.

Applications of Wavelet Analysis in Differential Propagation Phase Shift Data De-noising

Using numerical simulation data of the forward differential propagation shift （ΦDP） of polarimetric radar,the principle and performing steps of noise reduction by wavelet analysis are introduced in detail.Profiting from the multiscale analysis,various types of noises can be identified according to their characteristics in different scales,and suppressed in different resolutions by a penalty threshold strategy through which a fixed threshold value is applied,a default threshold strategy through which the threshold value is determined by the noise intensity,or a ΦDP penalty threshold strategy through which a special value is designed for ΦDP de-noising.Then,a hard-or soft-threshold function,depending on the de-noising purpose,is selected to reconstruct the signal.Combining the three noise suppression strategies and the two signal reconstruction functions,and without loss of generality,two schemes are presented to verify the de-noising effect by dbN wavelets：（1） the penalty threshold strategy with the soft threshold function scheme （PSS）; （2） the ΦDP penalty threshold strategy with the soft threshold function scheme （PPSS）.Furthermore,the wavelet de-noising is compared with the mean,median,Kalman,and finite impulse response （FIR） methods with simulation data and two actual cases.The results suggest that both of the two schemes perform well,especially when ΦDP data are simultaneously polluted by various scales and types of noises.A slight difference is that the PSS method can retain more detail,and the PPSS can smooth the signal more successfully.

Multi-scale phase average waveform of electroencephalogram signals in childhood absence epilepsy using wavelet transformation

BACKGROUND： Recent studies have focused on various methods of wavelet transformation for electroencephalogram （EEG） signals. However, there are very few studies reporting characteristics of multi-scale phase waves during epileptic discharge.OBJECTIVE： To extract multi-scale phase average waveforms from childhood absence epilepsy EEG signals between time and frequency domains using wavelet transformation, and to compare EEG signals of absence seizure with pre-epileptic seizure and normal children, and to quantify multi-scale phase average waveforms from childhood absence epilepsy EEG signals. DESIGN, TIME AND SETTING： The case-comparative experiment was performed at the Department of Neuroelectrophysiology, Tianjin Medical University from August 2002 to May 2005. PARTICIPANTS： A total of 15 patients with childhood absence epilepsy from the General Hospital of Tianjin Medical University were enrolled in the study. The patients were not administered anti-epileptic drugs or sedatives prior to EEG testing. In addition, 12 healthy, age- and gender-matched children were also enrolled.METHODS： EEG signals were tested on 15 patients with childhood absence epilepsy and 12 normal children. Epileptic discharge signals during clinical and subclinical seizures were collected 10 and 20 times, respectively. The collected EEG signals were treated with wavelet transformation to extract multi-scale characteristics during absence epilepsy seizure using a conditional sampling method. Multi-scale phase average waveforms were collected using a conditional phase averaging technique. Amplitude of phase average waveform from EEG signals of epilepsy seizure, subclinical epileptic discharge, and EEG signals of normal children were compared and statistically analyzed in the first half-cycle.MAIN OUTCOME MEASURES： Multi-scale wavelet coefficient and the evolution of EEG signals were observed during childhood absence epilepsy seizures using wavelet transformation. Multi-scale phase average waveforms from EEG signals were observed using a conditional sampling method and phase averaging technique.RESULTS： Multi-scale characteristics of EEG signals demonstrated that 12-scale （3 Hz） rhythmical activity was significantly enhanced during childhood absence epilepsy seizure and co-existed with background structure （〈1 Hz, low frequency discharge）. The phase average wave exhibited opposed phase abnormal rhythm at 3 Hz. Prior to childhood absence epilepsy seizure, EEG detected opposed abnormal a rhythm and 3 Hz composition, which were not detected with traditional EEG. Compared to EEG signals from normal children, epileptic discharges from clinical and subclinical childhood absence epilepsy seizures were positive and amplitude was significantly greater （P〈0.05）.CONCLUSION： Wavelet transformation was used to analyze EEG signals from childhood absence epilepsy to obtain multi-scale quantitative characteristics and phase average waveforms. Multi-scale wavelet coefficients of EEG signals correlated with childhood absence epilepsy seizure, and multi-scale waveforms prior to epilepsy seizure were similar to characteristics during the onset period. Compared to normal children, EEG signals during epilepsy seizure exhibited an opposed phase model.

Phase space reconstruction of chaotic dynamical system based on wavelet decomposition

In view of the disadvantages of the traditional phase space reconstruction method, this paper presents the method of phase space reconstruction based on the wavelet decomposition and indicates that the wavelet decomposition of chaotic dynamical system is essentially a projection of chaotic attractor on the axes of space opened by the wavelet filter vectors, which corresponds to the time-delayed embedding method of phase space reconstruction proposed by Packard and Takens. The experimental results show that, the structure of dynamical trajectory of chaotic system on the wavelet space is much similar to the original system, and the nonlinear invariants such as correlation dimension, Lyapunov exponent and Kolmogorov entropy are still reserved. It demonstrates that wavelet decomposition is effective for characterizing chaotic dynamical system.

A Construction Method of Wavelet with Linear Phase

H(ω) |is chosen as a filter for filter H(ω) ,and |H(ω)| is a even function when H(ω) is a real coefficient rational function of e -iω ,and so the scaling and wavelet functions corresponding to | H(ω) | possess linear phases. An algorithm is given for computation of wavelet at very low cost.

Methodology of Wavelets in Relativistic Heavy Ion Collisions in One Dimensional Phase Space (<i>η</i>-Space)

An attempt has been made to apply the wavelet methodology for the study of the results of the chaotic behavior of multiparticle production in relativistic heavy ion collisions. We reviewed the data that describes the collisions of relativistic heavy ion for the case η-space in 1-D phase space of variable. We compared the experimental data and UrQMD data using wavelet coherency. We discussed the results of the comparison.

Arrival time measurements of first arrival phases P and PKIKP using the method of fixed scale wavelet transformation ratio

The arrival times of first teleseismic phases are difficult to be measured precisely because of slowly and gradually changed onsets and weak amplitudes. The arrival times measured manually are usually behind the real ones. In this paper, using the ratio method of fixed scale wavelet transformations improved by us, the arrival times for the first arrival phases (such as P and PKIKP) at the teleseismic and far-teleseismic distances were measured. The results are reasonable and reliable based on the analysis and discussion of the reliabilities and errors.

A Wavelet Transform Method to Detect P and S-Phases in Three Component Seismic Data

The discrete time wavelet transform has been used to develop software that detects seismic P and S-phases. The detection algorithm is based on the enhanced amplitude and polarization information provided by the wavelet transform coefficients of the raw seismic data. The algorithm detects phases, determines arrival times and indicates the seismic event direction from three component seismic data that represents the ground displacement in three orthogonal directions. The essential concept is that strong features of the seismic signal are present in the wavelet coefficients across several scales of time and direction. The P-phase is detected by generating a function using polarization information while S-phase is detected by generating a function based on the transverse to radial amplitude ratio. These functions are shown to be very effective metrics in detecting P and S-phases and for determining their arrival times for low signal-to-noise arrivals. Results are compared with arrival times obtained by a human analyst as well as with a standard STA/LTA algorithm from local and regional earthquakes and found to be consistent.

个体化频带滑动窗特征的轻度认知障碍诊断研究

轻度认知障碍(MCI)是老年性痴呆诊断的关键阶段,脑电(EEG)信号特征可以反映MCI患者的认知状态,帮助实现早期诊断。现有研究在EEG特征提取过程中,针对脑电各节律,大多采用固定的时间窗完成分段处理,忽略了不同节律的特征差异,从而影响诊断效果。针对该问题,本文提出了一种新的组合滑动窗优化算法,该算法通过迭代振幅调整傅里叶变换(IAAFT)对零模型的构建方法进行了改进,以此得到评估大脑动态特性指标KPLI,通过对EEG各频段信号采取多种滑动窗组合,并以KPLI指标引导,得到适合不同频段的最佳滑动窗组合。在最佳滑动窗组合基础上,对各频段组合提取相位滞后指数(PLI),进行连续小波变换(CWT)特征,通过ResNet-MLP双通道分类网络实现MCI诊断。结果显示,使用个性化组合频段滑动窗对88名受试者(32名MCI患者,36名阿尔茨海默症患者以及20名正常对照组)实现了诊断分类,得到了82.2%的分类准确率,比固定窗的分类提高了10%(得到了72.2%的分类准确率)。结果表明,基于个体化脑电节律特征组合能够更好提取MCI的特征,提高轻度认知障碍诊断的正确率与特异性,是一种有效的脑电特征提取方法。

基于FTU的配电网单相接地故障定位与恢复方法

配电网发生最多的故障是接地故障。针对准确地检测隔离接地故障线路并尽快恢复供电,提出基于配电开关监控终端(FTU)的配电网单相接地故障定位与恢复方法。利用配电网各节点FTU的小波包变换暂态选线及相电流暂态特征选线技术,结合故障路径自适应处理控制策略,配合首开关延时合闸逻辑,从而实现多分支配电网故障定位与自动隔离恢复。所提方法能够快速选出故障线路并隔离故障区段,有利于故障快速消除,提高系统运行的安全性、可靠性。

Study of Wavelet Methodology and Chaotic Behavior of Produced Particles in Different Phase Spaces of Relativistic Heavy Ion Collisions

We applied the wavelet methodology for our earlier published research work of the chaotic behavior so called multiplicity fluctuations of secondary charged particles produced during the nucleus-nucleus (A-A) collisions at an energy of the order of ≈ 409 GeV in a new fashion. We illustrated the wavelet coherency in a relation of chaotic behavior for above said data of secondary charged pions in different phase spaces of collisions such as: η-space, φ-space (in one dimension) and ηφ-space (in two dimensions) respectively. We have shown the changes in the wavelet coherence when there are different values of two parameters “q” and “p”. We discussed our new results for the comparison purpose and findings were in the good agreements.

Phase-domain blind estimation of symbol duration based on Haar wavelet transform

A phase-domain blind estimator of symbol duration based on Haar wavelet transform（HWT） is proposed.It can estimate the symbol duration of phase modulated signals,such as M-ary phase-shift keying（MPSK） signals and polyphase coded signals.The closed form of the spectrum of HWT is derived.Theoretical analysis shows the frequency of the first spectral peak is equal to the symbol rate,which is the reciprocal of symbol duration.Thus the symbol duration can be extracted from the spectrum.Subsequently,the optimum wavelet scale is determined according to the maximum output signal to noise ratio（OSNR） criterion.MAT-LAB simulations show that this algorithm can blindly estimate the symbol duration without any prior knowledge.This estimator need not estimate the carrier frequency and has the characteristics of low computation complexity and high accuracy.

基于小波压缩深度学习重构的多图像光学加密

为解决多图像加密算法密文体积大、加密效果差、重构效果不理想等问题,提出一种基于小波压缩和深度学习重构的多图像光学加密方法。利用小波压缩提取多图像的低频部分,将重排后的新图放入改进的FDT-DRPE光学加密系统中得到密文;利用矢量分解和螺旋相位变换克服FDT-DRPE不敏感问题;构造的L_S混沌系统提高密钥敏感性。提出新型深度学习网络模型BHCN,解决传统图像重构精度不高问题。实验结果表明,密文体积可压缩至原图的1/4,重构图像的峰值信噪比为34.57 dB,结构相似性为0.9521,与同类文献相比,速度更快,重构效果更好,安全性更高。

基于电容层析测量的油气两相流动态信号时频分析方法

基于虚拟电容层析成像(electrical capacitance tomography,ECT)传感器和流电耦合仿真方法,本文动态模拟了4种典型的油气两相流流型及其相应的测量信号,并采用基于小波变换的多分辨率分析(multi-resolution analysis,MRA)和时频分析方法对压力和电容的时间序列信号进行分析。连续小波变换显示了时频二维平面中的能量分布,并在时间上有效定位了瞬时流量波动。而基于小波MRA方法对流型变化下的原始测量信号进行不同频带的分解后,压力和电容测量信号的主导频率均呈现出由中频到低频再到高频的迁移规律,为流型识别提供了有效的量化判据。在间歇流实验测量中应用信号时频分析方法,有效解析了不同流量下段塞频率、流量波动和液塞形态的变化。本文的研究方法和研究结果有望为实际过程中的流型识别和过程监测提供可靠手段。

应用遗传算法估计非稳态地震数据的混合相位子波及Q值

地震高分辨率处理或反演的目的是获得精确的反射系数或弹性参数模型。然而地层滤波效应模糊了地震记录中的地层反射信息,因此有必要消除这种滤波效应。现有的大部分提高分辨率方法并不能完全摒弃关于地震子波和Q模型的一些假设。为了获得更为切合实际的地震子波,同时自适应获取Q模型,文中将地震子波相位估计与Q模型估计相结合,提出了基于遗传算法的非稳态地震数据混合相位子波及Q值估计方法。首先通过井旁道记录拟合得到初始地震子波的振幅信息,然后依据子波Z变换的根关于单位圆移动与否构建用于遗传算法的编码链条。另一方面,十进制Q模型对应的二进制表示形式同样能够利用编码链条表征,因此利用全局优化算法能够同时估计地震混合相位子波以及Q模型。结合根变换和遗传算法不断改变子波相位的同时自适应生成Q模型,利用子波和Q模型构造的时变子波矩阵与测井反射系数得到合成地震记录,并与井旁道记录进行匹配,最终得到合理的混合相位子波和地层Q模型,进而构造时变子波矩阵进行时变反褶积。通过井旁道记录与测井数据拟合得到的混合相位子波,与实际地震子波相位更为接近,理论数据和实际数据处理结果证实了该方法的有效性。

岩性目标油气检测技术在琼东南盆地的应用

琼东南盆地超深水区Y目标储层为受多级断阶控制的高位域三角洲水下分流河道砂体,钻井资料揭示的气层和水层在地震资料上均表现为“亮点”,并具有远道增强的第Ⅲ类AVO特征,常规基于振幅信息的油气检测结果存在多解性。为此,提出一种充分利用地震数据的振幅、频率和相位信息的地震全信息烃类检测技术。首先,从地震正演出发综合分析砂岩储层叠后、叠前地震响应特征及其影响因素,指出孔隙度和气层厚度是影响利用振幅信息识别流体的主要原因,明确了基于振幅信息可以识别厚层含气砂岩;其次,通过地震反射频谱分析指出造成气层主频降低、频带变窄的主要原因是含烃引起的地震波衰减;最后,通过数值模拟表明厚层气砂和水层背景下的薄层气砂均表现为明显的吸收和相位异常,可以利用与地震波衰减相关的频率、相位信息开展含油气检测。研究区实际应用表明所提方法能够有效区分气层和水层,检测结果与实钻结果相吻合,为钻井实施提供了可靠依据,有效降低了勘探风险。

基于时域多分辨分析方法的长波信号时延预测

长波地波传播时延是决定陆基导航定位系统精度的关键,时域有限差分(Finite Difference-Time Domain,FDTD)方法可以提高其精度。但是FDTD方法在计算长距离的模型问题时迭代次数随之增多导致数值计算误差变大。主要通过基于圆柱坐标系下采用具有紧支撑特性的二阶矩Daubechies小波函数为尺度函数的时域多分辨分析(Multiresolution Time Domain,MRTD)方法来提高数值计算精度。随后对MRTD方法进行色散分析,最后将该方法应用于低频地波的传播预测中,提取观测点的衰减因子相位,与使用FDTD数值算法得到的结果进行对比,结果表明:MRTD方法可以在保持精度的前提下用时比FDTD更短。

饱和剩余振幅分析方法的研究及应用——以莺歌海盆地为例

为了理清目标评价中振幅的影响因素,识别莺歌海盆地的流体性质,基于保真保幅资料,开展了地震振幅的研究,建立了储层孔隙度、含气性、厚度、地层组合、子波相位与地震振幅响应的关系,形成了如下认识:1)随着孔隙度的增大,流体的振幅均逐渐增大;2)振幅在薄层的调谐厚度处最大,且振幅与厚度存在一定的线性关系;3)地层组合对振幅的影响,主要受地震波相互干涉;4)子波相位介于0°~90°时,振幅随其值的逐渐增大而增大,并且子波相位为90°时处最大;但介于90°~180°时,振幅逐渐减小,介于180°~360°重复上述规律。该成果认识获取了真实可靠的地层振幅信息,为勘探井位钻探提供了可靠技术支撑。