A new kind of wavelet-based method for spectrum deconvolution

To subtract the slit function from the measured spectrum, a wavelet-based deconvolution method is proposed to obtain a regularized solution of the problem. The method includes reconstructing the signal from the wavelet modulus maxima. For the purpose of maxima selection, the spatially selective noise filtration technique was used to distinguish modulus maxima produced by signal from the one created by noise. To test the method, sodium spectrum measured at a wide slit was deconvolved. He-Ne spectrum measured at the corresponding slit width was used as slit function. Sodium measured at a narrow slit was used as the reference spectrum. The deconvolutton result shows that this method can enhance the resolution of the degraded spectrum greatly.

Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation

In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener filter is used to obtain the ultrasonic reflectivity function through wavelet-based models. A new approach to the parameter estimation of the inverse filtering step is proposed in the nondestructive evaluation field, which is based on the theory of Fourier-Wavelet regularized deconvolution (ForWaRD). This new approach can be viewed as a solution to the open problem of adaptation of the ForWaRD framework to perform the convolution kernel estimation and deconvolution interdependently. The results indicate stable solutions of the esti- mated pulse and an improvement in the radio-frequency (RF) signal taking into account its signal-to-noise ratio (SNR) and axial resolution. Simulations and experiments showed that the proposed approach can provide robust and optimal estimates of the reflectivity function.

Fourier Self-deconvolution Using Approximation Obtained from Frequency Domain Wavelet Transform as a Linear Function

A new method of resolving overlapped peak, Fourier self-deconvolution （FSD） using approximation CN obtained from frequency domain wavelet transform of F（ω） yielded by Fourier transform of overlapped peak signals f（t） as the linear function, was presented in this paper. Compared with classical FSD, the new method exhibits excellent resolution for different overlapped peak signals such as HPLC signals, and have some characteristics such as an extensive applicability for any overlapped peak shape signals and a simple operation because of no selection procedure of the linear function. Its excellent resolution for those different overlapped peak signals is mainly because F（ω） obtained from Fourier transform of f（t） and CN obtained from wavelet transform of F（ω） have the similar linearity and peak width. The effect of those fake peaks can be eliminated by the algorithm proposed by authors. This method has good potential in the process of different overlapped peak signals.

Electrocardiogram Feature Extraction Technique Based on Wavelet Domain Lorentz Differential Deconvolution

In order to extract the cardiac characteristics in electrocardiogram (ECG), a feature extraction technique was developed based on wavelet domain Lorentz differential deconvolution. During the feature extraction of QRS complex, baseline drifts were firstly removed from raw ECG records by a mathematical morphology method and the feature sub-band of QRS complex was separated by using wavelet transform. Then an evolving Lorentz differential deconvolution technique was applied to estimating the local features of QRS complex from this sub-band. During the feature extraction of P and T waves, the above steps were similarly employed and, before wavelet transform, QRS complex was eliminated through locating their positions to avoid relevant disturbance. The proposed technique achieved a recognition of 99.37% for QRS recognition and a detection rate of 98.62% for P waves detection when tested with the MIT/BIH Database. And validated with the QT Database, the results of QT intervals detection also showed an obvious improvement (85.26% when target domain less than 14 ms and 95.34% when target domain less than 28 ms separately on average).

Potential risks of spectrum whitening deconvolution——compared with well-driven deconvolution

Deconvolution is widely used to increase the resolution of seismic data. To compare the resolution ability of conventional spectrum whitening deconvolution to thin layers with that of welldriven deconvolution, a complex sedimentary geological model was designed, and then the simulated seismic data were processed respectively by each of the two methods. The amplitude spectrum of seismic data was almost white after spectrum whitening, but the wavelet resolution was low. The amplitude spectrum after well-driven deconvolution deviated from white spectrum, but the wavelet resolution was high. Further analysis showed that if an actual reflectivity series could not well satisfy the hypothesis of white spectrum, spectrum whitening deconvolution had a potential risk of wavelet distortion, which might lead to a pitfall in high resolution seismic data interpretation. On the other hand, the wavelet after well- driven deconvolution had higher resolution both in the time and frequency domains. It is favorable for high resolution seismic interpretation and reservoir prediction.

Multiscale anisotropic diffusion for ringing artifact suppression in geophysical deconvolution data

Ringing artifact degradations always appear in the deconvolution of geophysical data. To address this problem, we propose a postprocessing approach to suppress ringing artifacts that uses a novel anisotropic diffusion based on a stationary wavelet transform （SWT） algorithm. In this paper, we discuss the ringing artifact suppression problem and analyze the characteristics of the deconvolu- tion ringing artifact. The deconvolution data containing ringing artifacts are decomposed into different SWT sub- bands for analysis, and a new multiscale adaptive aniso- tropic filter is developed to suppress these degradations. Finally, we demonstrate the performance of the proposed method and describe the experiments in detail.

The preconditioned conjugate gradient deconvolution method and its application

The preconditioned conjugate gradient deconvolution method combines the realization of sparse deconvolution and the optimal preconditioned conjugate gradient method to invert to reflection coefficients. This method can enhance the frequency of seismic data processing and widen the valid frequency bandwidth. Considering the time-varying nature of seismic signals, we replace the constant wavelet with a multi-scale time-varying wavelet during deconvolution. Numerical tests show that this method can obtain good application results.

Blind Deconvolution of Seismic Data Based on the Spearman’s Rho

In this paper, we propose a novel seismic blind deconvolution approach based on the Spearman’s rho in the case of band-limited seismic data with a low dominant frequency and short data records. The Spearman’s rho is a measure of the dependence between two continuous random variables without the influence of the marginal distributions, by which a new criterion for blind deconvolution is constructed. The optimization program for new criterion of blind deconvolution is performed by applying Neidell’s wavelet model to the inverse filter. The noise-free and noisy synthetic data, onshore seismic trace in the Ordos Basin, and offshore stacked section in the Bohai Bay Basin examples show good results of the method.

基于谐波分解恢复弱势信号的高分辨率处理技术

地震资料高分辨率处理是预测薄储集层的有效手段,高分辨率处理的主要目的是有效恢复地震高、低频信息和拓宽频带,同时保持资料的信噪比和保真度。基于谐波分解恢复弱势信号的高分辨率处理技术,以压缩小波变换为基础,根据信号谐波分量对地震高、低频弱势信号进行恢复。首先,基于有效频带内的地震信号,采用压缩小波变换将其分解为各基频信号,然后计算各基频信号的高次谐波与低次谐波,将计算出的高次谐波与低次谐波加入小波变换系数中,最后进行小波逆变换,即可恢复高、低频弱势信号。在实现过程中,仅仅在有效频带内估算基频信号,能较好地保持信号的信噪比。地震信号的小波变换系数与地层反射系数具有一致性,因此该技术具有较高的保真度,可以较好保持相对振幅关系。实际应用表明,在保持资料信噪比的同时,该技术能大幅拓宽地震资料的频带,处理后的地震剖面断点更清楚,分辨能力改善明显,能较好地识别6 000 m以深约40 m的薄储集层。

基于小波与反褶积结合的薄互层岩性界面识别方法

准噶尔盆地莫西庄地区三工河组二段岩性复杂多样、变化快,单砂岩体中薄层岩性频繁互层,存在较强的岩性、物性、含油非均质性特征。精细划分薄互层岩性并识别其分层界面是开展储层识别、流体解释及有效储层厚度确立等重要的前提工作。为了精确地识别薄互层岩性的分层界面,从测井信息采集分辨率与信号分析角度出发,提出了基于小波与反褶积相结合的薄互层岩性分层界面识别方法。首先,利用小波变换多尺度分解和重构原理,对自然伽马测井曲线进行高、低频分解,提取出反映地层岩性变化的有效信号与高频噪声;然后,对有效信号进行重构,构建一条去除噪声能够表征更接近真实地层信息的自然伽马测井曲线;最后,利用反褶积方法对重构的自然伽马测井曲线进行高分辨率处理,很大程度上提高了自然伽马测井曲线响应薄层岩性的纵向分辨率。经实例井取心岩性资料验证,利用该方法处理得到高分辨率自然伽马测井曲线的形态与数值特征能够有效地提高单砂岩体中多套薄互层岩性分层界面识别的精度,为莫西庄三工河组二段储层有效性精细评价提供了依据。

一种基于改进门控循环单元的叠前时变子波提取方法

子波的精确提取是地震勘探后续反演与成像的前提,针对传统时变子波提取方法受到的各类假设限制,且需分别提取子波振幅谱与相位谱的问题,本文提出了一种基于改进门控循环单元(GRU)网络的叠前时变地震子波提取方法.根据实际叠前地震数据分布特征与非平稳性质,本方法首先建立非平稳地震记录与添加随机噪声的时变子波训练数据集;为对提取出的时序特征进行拓展,提升传统GRU网络对长时序列的处理能力,本方法搭建起含多层GRU模块与全连接神经网络的改进门控循环单元网络模型;利用建立的训练数据集对网络模型进行训练使网络具备提取时变子波的能力;为提高训练效率与提取精度,本方法在训练的反向传播过程中应用自定义WaveLoss损失函数衡量误差,最终实现叠前时变子波的估计.经合成数据仿真实验与不同方法对比验证,本文提出的叠前时变子波提取方法具有更高的准确度;经对中国西部不同地区实际叠前地震资料处理与反褶积验证分析,该方法可有效提高目标区叠前地震剖面分辨率.

基于IEWT-MOMEDA-FSC的滚动轴承故障诊断

针对滚动轴承故障信号常伴有噪声干扰且故障特征难以提取的问题,本文提出一种基于改进经验小波变换(IEWT)、多点优化最小熵解卷积(MOMEDA)和快速谱相关(FSC)的滚动轴承故障诊断方法.首先,将原始信号进行快速谱相关分析得到增强包络谱,通过增强包络谱的极值点来自适应地划分频谱,以分割的频谱为边界构建小波滤波器组将信号分解为多个IMF分量,利用相关峭度准则筛选出有效的分量进行叠加;其次,用MOMEDA对其进行降噪处理,将降噪后的信号进行快速谱相关分析,得到增强包络谱图;最后,将增强包络谱图中幅值较高的频率与故障频率对比,判定其失效形式,用所提出的方法对实测轴承故障信号进行分析验证.结果表明,所提出的方法能有效降低噪音干扰且增强信号故障冲击特性,在噪声环境下具有较强的故障特征提取能力.

基于小波去噪和反卷积的光声成像优化算法

光声成像因兼具光学成像高对比度和超声成像高分辨率的优点而成为一种被广泛关注的无损成像技术。针对时间反转光声成像过程中存在的噪声污染和脉冲响应影响问题,通过建立光声压数据采集模型,将光声压数据的小波软阈值去噪和反卷积计算相结合来去除光声压数据中的噪声和脉冲响应影响。反卷积计算时设定可调节参数,使计算结果随着光声压的傅里叶系数大小不同而调整,从而进一步提升重建光声图像的质量。仿真实验结果表明,改进的时间反转光声成像算法,能有效减少重建光声图像中的噪声和伪影,明显提高重建光声图像的可视效果。

基于小波包分解和MCKD的水泵轴承故障诊断方法

针对水泵在实际应用中所处环境复杂、故障信号包含大量噪声难以提取的问题,提出了一种结合小波包分解和最大相关峭度解卷积(MCKD)的水泵轴承故障诊断方法。首先,应用小波包分解对原始信号进行分解,根据分解信号的信噪比和标准差选取合适的分量进行重构;然后,采用MCKD算法对重构信号降噪处理,突出信号中的有效周期冲击成分;最后,对处理好的信号进行包络谱分析,从包络谱中得到故障频率。实验结果表明,小波包分解和MCKD方法能够有效提取水泵轴承故障特征频率,可为工程实际应用提供参考。

基于频谱分析的煤矿螺旋滚筒洗煤机故障振动诊断

洗煤过程一个多因素、多参数相互耦合的复杂过程,会对洗煤机的两轴产生干扰,使两轴出现不同心问题,使洗煤机联轴器出现故障,导致出现振动现象。为及时发现和解决设备故障、避免因设备故障导致的生产停滞和产品质量下降,提出基于频谱分析的煤矿螺旋滚筒洗煤机故障振动诊断方法。该方法利用最小熵解卷积、小波去噪及自回归线性预测对不同种类的煤矿螺旋滚筒洗煤机振动信号特征实施增强处理,加强洗煤机振动信号,使之更加清晰可辨,凸显其故障特征。在此基础上,对振动信号进行频谱分析,获得信号的时域波动,从而更直观地观察振动信号的频谱特征,克服振动信号非线性和非平稳行的特点,实现精准的煤矿螺旋滚筒洗煤机故障振动诊断。实验结果表明,该方法的异常振动信号频谱分析能力强,能够精准诊断不同故障。

多元多通道反卷积模型中小波估计的相合性

针对一类多通道反卷积模型中多元信号的估计问题,利用傅里叶分析、加权求和与小波投影方法,构造了线性小波估计器。在不假定未知信号光滑性的条件下,证明了上述小波估计器在普通光滑、超光滑2种情形均满足平均L^(p)(1≤p<∞)相合性。该结论为研究小波估计器的收敛阶提供了理论支持。

地震拓频技术在苏X区块含煤致密砂岩储层预测中的应用

苏X区块目的层是典型的低孔、低渗、非均质性强的储层,并且其地表为沙漠、草地环境,储层内煤层发育,这些因素均对地震信号产生严重屏蔽作用,导致地震资料分辨率低、信噪比低、砂体地震响应特征不明显,从而增加了储层预测的难度。针对这些问题,对三维地震数据分步骤进行处理:首先采用子波分解技术去除煤层的屏蔽效应;然后采用时变分频反褶积技术进行拓频处理,提高地震数据分辨率;最后采用Alpha滤波去噪技术提高地震资料信噪比。通过三步数据处理后,地震资料的品质得到提升,地震信息更加丰富,地震属性更为精细,地震反射层位的地质意义更加明确,使得储层预测结果更为精准。应用效果表明,地震拓频技术适用于含煤致密砂岩储层的精细预测。

基于MMED+TQWT方法的叶轮振动信号特征提取研究

为了提高高炉煤气余压发电装置(TRT)叶轮故障诊断能力,开发了以最小熵解卷积MMED和可调品质因子小波变换TQWT两种方法共同诊断叶轮故障的技术。先利用MMED方法转换初始振动信号获得更明显的故障冲击成分,再对经过预处理的信号实施TQWT分解,并设定相应的品质因子Q,再按照峭度最大原则确定子带最优分量并计算包络谱数据,实现叶轮故障的诊断功能。研究结果表明:采用本文方法分析故障冲击成分获得了显著增强,对噪声干扰起到了明显抑制作用。从包络谱内明显看到跟叶轮故障特征频率相同的频率特征,形成了明显的边频带,说明叶轮中已形成故障特征。

Wavelet Analyses of Geomagnetic Data regarding Major Geomagnetic Disturbances

The purpose of this study was to analyze the associated spectrum of geomagnetic field,frequencies intensity and the time of occurrence.We calculated the variation of the correlation coefficients,with mobile windows of various sizes,for the recorded magnetic components at different latitudes and latitudes.The observatories we included in our study are USA(Surlari),HON(Honolulu),SBA(Scott Base),KAK(Kakioka),THY(Tihany),UPS(Uppsala),WNG(Wingst)and Yellowknife(YKC).We used the data of these observatories from International Real-time Magnetic Observatory Network(INTERMAGNET)for the geomagnetic storm from October 28-31,2003.We have used for this purpose a series of filtering algorithms,spectral analysis and wavelet with different mother functions at different levels.In the paper,we show the Fourier and wavelet analysis of geomagnetic data recorded at different observatories regarding geomagnetic storms.Fourier analysis hightlights predominant frequencies of magnetic field components.Wavelet analysis provides information about the frequency ranges of magnetic fields,which contain long time intervals for medium frequency information and short time intervals for highlight frequencies,details of the analyzed signals.Also,the wavelet analysis allows us to decompose geomagnetic signals in different waves.The analyses presented are significant for the studies of the geomagnetic storm.The data for the next days after the storm showed a mitigation of the perturbations and a transition to quiet days of the geomagnetic field.

基于连续交叉小波相干分析和自适应CYCBD的轴承故障诊断

最大二阶循环平稳指标盲解卷积(maximum second-order cyclostationarity blind deconvolution,CYCBD)能从强背景噪声信号中恢复周期脉冲,是轴承故障诊断的有效方法。故障特征频率是CYCBD的关键参数,由于滚动轴承存在制造误差、滚子滑移等现象,导致真实的故障特征频率与理论值存在偏差,降低了CYCBD的有效性。同时,故障轴承测试信号中含有大量噪声和谐波干扰,也降低了CYCBD的故障特征提取能力。对此,提出了一种基于连续交叉小波相干分析和自适应CYCBD的轴承故障诊断方法,首先,利用正常轴承、故障轴承测试信号的交叉小波相干分析获取轴承故障共振频带。其次,基于3种归一化的周期检测指标提出一种新的周期检测技术以获取真实的轴承故障特征频率。最后,基于轴承故障共振频带信号和真实轴承故障特征频率进行CYCBD滤波,并针对滤波信号进行Teager能量算子解调分析得到能量频谱,从而进行轴承故障诊断。仿真信号和高速列车牵引电机轴承试验信号的分析结果表明,该方法能够有效识别轴承故障特征,且优于传统的CYCBD方法。