Ground roll wave suppression based on wavelet frequency division and radial trace transform

Ground roll waves interfere with seismic data. The suppression of ground roll waves based on the division of wavelet frequencies considers the low-frequency characteristics of ground roll waves. However, this method will not be effective when the ground roll wave and the effective signal have the same frequency bands because of overlapping. The radial trace transform （RTT） considers the apparent velocity difference between the effective signal and the ground roll wave to suppress the latter, but affects the low-frequency components of the former. This study proposes a ground roll wave suppression method by combining the wavelet frequency division and the RTT based on the difference between the ground roll wave velocity and the effective signal and their energy difference in the wavelet domain, thus making full use of the advantages of both methods. First, we decompose the seismic data into different frequency bands through wavelet transform. Second, the RTT and low-cut filtering are applied to the low-frequency band, where the ground roll waves are appearing. Third, we reconstruct the seismic record without ground roll waves by using the inverse RTT and the remaining frequency bands. The proposed method not only improves the ground roll wave suppression, but also protects the signal integrity. The numerical simulation and real seismic data processing results suggest that the proposed method has a strong ability to denoise while preserving the amplitude.

OFDM Scheme Based on Wavelet Packet Transform-Oriented Graded Multi-Service

In this paper, a concept of image grading transmission is put forward to enhance data rate and to improve the usage of subcarriers in orthogonal frequency division multiplexing (OFDM). The idea originates from the wavelet packets representative of an image in which information is graded in terms of different priorities. The graded image facilitates more efficient use of adaptive subcarriers and bits allocation. The results of simulation in typical mobile environment prove that the output signal noise ratio (SNR) of the graded image excels that of the ungraded image by 1—2 dB under the same channel condition.

A NOVEL ALGORITHM OF MULTI-SENSOR IMAGE FUSION BASED ON WAVELET PACKET TRANSFORM

In order to enhance the image information from multi-sensor and to improve the abilities of the information analysis and the feature extraction, this letter proposed a new fusion approach in pixel level by means of the Wavelet Packet Transform (WPT). The WPT is able to decompose an image into low frequency band and high frequency band in higher scale. It offers a more precise method for image analysis than Wavelet Transform (WT). Firstly, the proposed approach employs HIS (Hue, Intensity, Saturation) transform to obtain the intensity component of CBERS (China-Brazil Earth Resource Satellite) multi-spectral image. Then WPT transform is employed to decompose the intensity component and SPOT (Systeme Pour I'Observation de la Therre ) image into low frequency band and high frequency band in three levels. Next, two high frequency coefficients and low frequency coefficients of the images are combined by linear weighting strategies. Finally, the fused image is obtained with inverse WPT and inverse HIS. The results show the new approach can fuse details of input image successfully, and thereby can obtain a more satisfactory result than that of HM (Histogram Matched)-based fusion algorithm and WT-based fusion approach.

A New HFRT Algorithm Based on Maximal Overlap Discrete Wavelet Packet Transformation

The high frequency resonant technique （HFRT） algorithm is a popular technique for fault-detection and is widely applied in mechanism systems and industrial constructions. In this paper, a new HFRT algorithm based on maximal overlap discrete wavelet packet transformation （MODWPT） is developed. By the simulation test for soil embedded pipes fault-detection, it is shown that the performance of newly proposed HFRT algorithms is more sensitive to early defects than the traditional HFRT methods based on the Hilbert transform.

Impulse Response Identification Based on Varying Scale Orthogonal Wavelet Packet Transform

In this paper, by applying a group of specific orthogonal wavelet packet to Eykho?algorithm, a new impulse response identification algorithm based on varying scale orthogonal WPTis provided. In comparison to Eykho? algorithm, the new algorithm has better practicability andwider application range. Simulation results show that the proposed impulse response identificationalgorithm can be applied to both deterministic and random systems, and is of higher identificationprecision, stronger anti-noise interference ability and better system dynamic tracking property.

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

Generation of artificial earthquakes for matching target response unsmooth spectrum via wavelet package transform

The seismic records of target response spectrum used in the time-history analysis should be allowed to meet the norms. However, the previous fitting methods of target spectrum are mostly for the situations that the target spectrum is a smooth curve. In many cases, it needs to match unsmooth target spectrum for single determined response spectrum. An adjustment of time history via wavelet packet transform was presented, which is able to fit unsmooth target spectrum. It was found that there is a certain bias between the band center frequency of the component of seismic record after wavelet packet decomposition and the peak frequency of response spectra of wavelet packet components. For this reason, five strategies were presented to select iteration points, and the effects of the five strategies were compared with two calculation examples. It was turned out that the peak frequency of the response spectrum of wavelet packet component can lead to good fitting effect when it is selected as the iteration point. In the iteration process, it shows great promise in fitting non-smooth target spectrum and has a trend of converge.

Walsh Hadamard Transform Based Transceiver Design for SC-FDMA with Discrete Wavelet Transform

This article proposes a new transceiver design for Single carrier frequency division multiple access(SCFDMA)system based on discrete wavelet transform(DWT). SCFDMA offers almost same structure as Orthogonal frequency division multiple access(OFDMA)with extra advantage of low Peak to Average Power Ratio(PAPR). Moreover,this article also suggests the application of Walsh Hadamard transform(WHT)for linear precoding(LP)to improve the PAPR performance of the system. Supremacy of the proposed transceiver over conventional Fast Fourier transform(FFT)based SCFDMA is shown through simulated results in terms of PAPR,spectral efficiency(SE)and bit error rate(BER).

一种适用于混合双极直流输电的纵联方向保护方法

直流输电线路通常是以大电感和直流滤波器作为边界,故障行波在边界发生折射和反射时,其行波能量也会因为电感和滤波器的存在而被吸收,导致区内、外高频行波能量产生差异.当故障发生为正方向故障时,其高频前行波能量小于高频反行波能量;当故障发生为反方向故障时,其高频前行波能量大于高频反行波能量.由此特征提出了一种基于前、反行波高频能量差异的纵联方向保护,该保护采用小波包变换提取行波的高频能量,利用前行波与反行波在区内故障和区外故障时的高频能量差异判别故障.仿真结果表明,该保护能快速准确地识别故障,并且不受故障类型、过渡电阻以及故障发生位置距离和噪声的影响,具有一定的实用性.

脑电信号多特征融合与卷积神经网络算法研究

针对脑电信号(electroencephalogram,EEG)运动想象中单一特征无法多维表征信号中的信息导致的分类准确率不高的问题,提出一种基于样本熵和共空间模式特征融合的特征提取算法。算法先对原始脑电信号进行小波包分解,从中选择包含μ和β节律的分量进行重构,然后分别提取重构信号的样本熵和CSP(common spatial pattern,CSP)特征,将两者融合组成新的特征向量,使用所设计的一维卷积神经网络对其进行识别获得分类结果。所提方法在2003年BCI Dataset III中获得了91.66%的分类准确率,在2008年BCI Dataset A中获得了85.29%的平均分类准确率。与近年来文献中提出的多特征融合算法相比,准确率提高了7.96个百分点。

基于LWT的可见光OFDM-IM系统研究

针对索引调制正交频分复用(OFDM-IM)技术峰均比过高,对相位噪声和载波频偏敏感,系统复杂度高等问题,提出基于小波提升变换(LWT)的可见光OFDM-IM系统。首先选择正交小波基作为子载波,通过对信号的分裂、预测和更新,将信号分为高频信号和低频信号,再结合可见光信道构成LWT-OFDM-IM系统,最后通过理论分析和蒙特卡罗方法对系统的可靠性、峰均比特性和最佳小波分解层数进行了仿真验证,结果表明:当子载波N=256,子块中子载波数量L=4,激活子载波数量k=2,系统误码率为10~(-4)时,LWT-OFDM-IM比FFT-OFDM-IM改善约8 dB,相较于DWT-OFDM-IM提高约4 dB。当系统互补累计分布函数(CCDF)为10^(-1)数量级时,LWT-OFDM-IM的峰均比较FFT-OFDM-IM改善约2.3 dB。随着小波层数的递增,系统的误码性能越好,当小波层数为3层时,大约在10 dB附近,误码率可到10^(-5)量级。

基于小波包理论研究地下断层对爆破振动传播的影响

为了研究云南某金属地下矿山断层对爆破振动波传播的影响,选用含有断层区域3736北部中段区域、无断层区域3736南部中段区域布置测点。以云南某金属地下矿山真实采集爆破振动数据为研究对象,基于小波包变换、HHT算法、STFT算法开展爆破振动波衰减规律的研究。结果表明:云南某地下金属矿山振动波经过断层前瞬时能量幅值达到10×10^(-4)dB,经过断层后瞬时能量幅值达到1.5×10^(-4)dB,瞬时能量幅值衰减85%;断层在一定程度上会扰乱振动波能量分布情况,延缓振动能量迅速上升;振动波经过断层后低频能量衰减60%,高频能量衰减71%,总体能量功率衰减77%,无断层区域高频能量衰减18%,低频能量衰减17%,总体能量功率衰减15%;爆破振动波经过断层后,过滤大量高频能量,能量总体分布呈现向低频域方向发展的趋势。

基于多频带多特征融合的配电网单相接地故障选段方法

针对现有配电网单相接地故障选段方法存在的通信负担重、阈值设置困难及故障特征量单一等问题,提出一种基于多频带多特征融合的配电网单相接地故障选段方法。首先,利用小波包变换分别对故障后首半个工频周期的零序电压导数和零序电流进行分解和重构,得到原始波形的不同频带分量。其次,根据不同频带分量间的极性关系分别引入伏安特性特征向量与零序功率累加和作为故障特征。最后,筛选特征频带并构造基于特征频带与故障特征融合的选段判据。仿真实验及现场数据验证结果表明,所提方法能够有效实现单相接地故障免阈值就地选段。

非均匀间歇采样转发干扰对脉内捷变雷达影响分析

脉内频率捷变雷达通过自身“主动”抗干扰特性,利用传统间歇采样转发干扰的不连续性和周期性的特点,结合抗干扰算法,可以有效抑制间歇采样转发干扰。为提高干扰机对脉内频率捷变雷达的干扰效能,本文提出采用非均匀间歇采样转发干扰对脉内频率捷变雷达进行干扰,首先对截获到的脉内频率捷变雷达信号通过时频脊线提取与小波变换进行参数估计,获取子脉冲宽度,然后根据子脉冲宽度约束干扰参数,构造非均匀间歇采样转发干扰。理论分析和结果表明,通过非均匀间歇采样转发使得脉内频率捷变雷达在时频域等多域难以抑制干扰,极大地提升了干扰机的干扰能力。

Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning

A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.

偏振图像融合的人脸图像增强研究

为提升人脸图像的整体质量,便于人脸图像的快速精准识别与处理,提出了基于偏振图像融合的人脸图像增强方法。首先获得人脸光强图像与偏振度图像,结合小波包变换方法分解和融合两种人脸图像的高、低频信息,实现人脸图像的增强处理。实验结果表明,该方法所获得的人脸增强图像可保留原有图像的低频信息,增强后的人脸图像清晰度高,脸部细节与轮廓特征明显,信息量与反差对比度较高,整体综合质量较好,增强效果较为理想。

川东地区寒武系高台组白云岩-蒸发岩共生地层高频层序划分及地质意义

白云岩-蒸发岩共生地层中油气资源丰富。基于层序地层学原理,结合钻井、薄片等资料,在三级层序划分的基础上,采用INPEFA和小波变换技术对川东地区寒武系高台组白云岩-蒸发岩共生地层的高频层序进行识别与划分,并探讨了其地质意义。研究结果表明:(1)川东地区寒武系高台组白云岩-蒸发岩共生地层可划分为3个三级层序,自下而上分别为Sq1,Sq2,Sq3,可进一步划分为6个四级层序(ssq1—ssq6)。高台组主要为碳酸盐岩台地沉积环境,自西向东依次发育混积台地、局限-蒸发台地、开阔台地相沉积。(2)研究区高台组的岩性组合序列包括白云岩上覆厚层蒸发岩(A)、厚层白云岩夹蒸发岩(B)、白云岩与蒸发岩互层(C)和蒸发岩上覆厚层白云岩(D)等4种类型。白云岩-蒸发岩共生地层发育在四级层序的接触界面,且多发育于层序内部的高位体系域,反映了海平面较强烈的扰动,尤其是海平面的相对下降有利于白云岩和蒸发岩沉积。(3)研究区高台组白云岩-蒸发岩共生地层的岩性组合序列在GR_(inpefa)曲线和小波变换曲线上均具有响应特征,对应的64尺度小波系数曲线波动平缓,能量团为弱黄蓝色,GR_(inpefa)曲线以“缺口”的形式表现出蒸发岩与白云岩的岩性差异。

机器学习模型k近邻算法分析脑电图对主观性耳鸣的诊断价值

目的探讨机器学习模型k近邻算法分析脑电图对主观性耳鸣的诊断价值。方法纳入主观性耳鸣患者87例(耳鸣组),健康受试者91例(对照组)。使用MATLAB和EEGLAB工具箱、小波包变换和样本熵相结合的方法分析两组δ、θ、α1、α2、β1、β2、β3、γ频段在耳鸣发生网络相关7个区域的样本熵差异。对耳鸣脑电图特征数据使用Python的scikit-learn包进行k近邻算法分析,使用准确率、召回率、精确度和F1得分评估k近邻算法对主观性耳鸣的诊断价值。结果两组样本熵在左听觉、左额叶、中央、右顶叶和左顶叶等区域差异有显著性(P<0.05)。耳鸣组δ、α2和β1节律平均熵大于对照组,θ、α1、β2、β3和γ节律平均熵小于对照组(P<0.05)。耳鸣组和对照组样本熵在FC5、C1、CP1和P4单通道中差异有显著性(P<0.05)。k近邻算法对主观性耳鸣的诊断准确率为91.98%,召回率为90.24%,准确率为96.28%,F1得分为93.12%。结论机器学习模型k近邻算法分析脑电图结果可以辅助临床医生对耳鸣进行诊断。

分频AVO技术在安岳气田须二段储层含气性分析中的应用

四川盆地安岳气田须二段致密气储量丰富,地质特征相对复杂,常规AVO分析方法预测储层含气性分布的精度较低,需要研究采用更加精细的地震预测方法。基于单井岩石物理和正演模型特征分析,采用基于小波变换下的分频AVO反演,优选出优势频段的AVO属性进行融合构建含气性因子,进而对安岳气田须二段有利含气富集区带进行分布预测。结果表明,须二段气层主要表现为Ⅳ类AVO响应异常。优势频段35~45 Hz的气、水层AVO响应比全频段的AVO响应的差异特征较明显,差异性更大,更易突出含气响应特征;AVO含气性敏感属性为相对横波速度差异、相对泊松比差和流体因子,经融合得到含气指示因子的负异常区域指示含气有利区;井震对比可知优势频段AVO属性的含气性预测效果较好。该方法以期为非常规油气勘探提供技术支撑。

基于EZW算法的叠前地震数据分频压缩技术

随着高密度采集技术的发展及采集日效的大幅提高,勘探数据量呈指数增长。为了节省存储空间,提高传输效率,叠前地震数据的高保真压缩与重构研究显得极其迫切。嵌入式零树小波(embedded zerotree wavelet, EZW)算法具有优越的压缩性能,传统的EZW地震数据压缩基于t-x域(时间-空间域)数据。由于叠前地震数据的波数较宽,频带有限,经离散余弦变换(discrete cosine transform, DCT)后,能量主要集中在低频部分。本文提出一种分频地震数据压缩技术,通过2D-DCT将t-x域数据转换为DCT域数据并进行分频,对高频和低频数据单独进行EZW编码。低频数据编码级数高,保证重构数据的信噪比;高频数据编码级数低,提高数据的压缩比。模型数据和实际数据表明,在解压信噪比基本相当的情况下,分频地震数据压缩的压缩比高于常规EZW压缩方法。