AMicroseismic Signal Denoising Algorithm Combining VMD and Wavelet Threshold Denoising Optimized by BWOA

The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized VariationalMode Decomposition(VMD)jointWavelet Threshold Denoising(WTD)algorithm(BVW)is proposed.The BVW algorithm integrates VMD and WTD,both of which are optimized by BWOA.Specifically,this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited IntrinsicMode Functions(BLIMFs).Subsequently,these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold are selected as the effective mode functions,and the effective mode functions are denoised using WTD to filter out the residual low-and intermediate-frequency noise.Finally,the denoised microseismic signal is obtained through reconstruction.The ideal values of VMD parameters and WTD parameters are acquired by searching with BWOA to achieve the best VMD decomposition performance and solve the problem of relying on experience and requiring a large workload in the application of the WTD algorithm.The outcomes of simulated experiments indicate that this algorithm is capable of achieving good denoising performance under noise of different intensities,and the denoising performance is significantly better than the commonly used VMD and Empirical Mode Decomposition(EMD)algorithms.The BVW algorithm is more efficient in filtering noise,the waveform after denoising is smoother,the amplitude of the waveform is the closest to the original signal,and the signal-to-noise ratio(SNR)and the root mean square error after denoising are more satisfying.The case based on Fankou Lead-Zinc Mine shows that for microseismic signals with different intensities of noise monitored on-site,compared with VMD and EMD,the BVW algorithm ismore efficient in filtering noise,and the SNR after denoising is higher.

Prediction of Tight Sand Reservoir with Multi-Wavelet Decomposition and Reconstructing Method

Special reservoir or fluid has an abnormal response to some certain frequencies, so that seismic decomposition and reconstruction are used to highlight the seismic reflection at certain frequencies useful to identify special geological bodies. Because seismic wavelets are time-varying and spatial-variable in the propagation, synthetic traces based on single wavelet make some weak but useful information lost, and make artifacts form. However, Morlet wavelet aggregation with mathematical analytical expression is able to fully and correctly reflect the variations of wavelet in the propagation of underground medium. The matching pursuit algorithm on the basis of Morlet wavelet improves the calculating efficiency in decomposition and reconstruction greatly. This method is applied to the actual study area to do conjoint analysis of single well and well-tie multi-wavelet decomposition. It is found that frequencies sensitive to interest reservoirs range from 8 to 34 Hz. Reconstructing the wavelets at those special frequencies and analyzing the reconstructed seismic data, it is pointed out that interest reservoirs have abnormal characteristics with respectively strong RMS amplitude in the reconstructed data. Crossplot of gamma value at wells and reconstructed RMS amplitude suggests that anomalies caused by interest reservoirs are well separated from the background anomalies when the reconstructed RMS amplitude is greater than 3650. Quantitative prediction results of interest reservoirs distribution in the study area reveal that interest reservoirs of western and northern study area are distributed annularly and bandedly, while most contiguous sandstone in eastern regions appears sporadically.

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.

Improving wavelet reconstruction algorithm to achieve comprehensive application of thermal infrared remote sensing data from TM and MODIS

According to the data characteristics of Landsat thematic mapper(TM) and MODIS,a new fusion algorithm about thermal infrared data has been proposed in the article based on improving wavelet reconstruction.Under the domain of neighborhood wavelet reconstruction,data of TM and MODIS are divided into three layers using wavelet decomposition.The texture infonnation of TM data is retained by fusing high-frequency information.The neighborhood correction coefficient method(NCCM) is set up based on the search neighborhood of a certain size to fuse low-frequency information.Thermal infrared value of MODIS data is reduced to the space value of TM data by applying NCCM.The data with high spectrum,high spatial and high temporal resolution,are obtained through the algorithm in the paper.Verification results show that the texture information of TM data and high spectral information of MODIS data could be preserved well by the fusion algorithm.This article could provide technical support for high precision and fast extraction of the surface environment parameters.

Application of Wavelet Decomposition to Removing Barometric and Tidal Response in Borehole Water Level

Wavelet decomposition is used to analyze barometric fluctuation and earth tidal response in borehole water level changes. We apply wavelet analysis method to the decomposition of barometric fluctuation and earth tidal response into several temporal series in different frequency ranges. Barometric and tidal coefficients in different frequency ranges are computed with least squares method to remove barometric and tidal response. Comparing this method with general linear regression analysis method, we find wavelet analysis method can efficiently remove barometric and earth tidal response in borehole water level. Wavelet analysis method is based on wave theory and vibration theories. It not only considers the frequency characteristic of the observed data but also the temporal characteristic, and it can get barometric and tidal coefficients in different frequency ranges. This method has definite physical meaning.

Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-synchronous Oscillation Modes Using PMU Data

This paper proposes an empirical wavelet transform(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)data.The phasors from PMUs are preprocessed to check for the presence of oscillations.If the presence is established,the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm.The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China.Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

A novel wavelet method for electric signals analysis in underwater arc welding

Electric signals are acquired and analyzed in order to monitor the underwater arc welding process. Voltage break point and magnitude are extracted by detecting arc voltage singularity through the modulus maximum wavelet （MMW） method. A novel threshold algorithm, which compromises the hard-threshold wavelet （HTW） and soft-threshold wavelet （STW） methods, is investigated to eliminate welding current noise. Finally, advantages over traditional wavelet methods are verified by both simulation and experimental results.

VMD-Wavelet联合去噪算法研究与应用

为解决天然气管道运行过程中采集到的泄漏声波信号含有大量噪声的问题,通过研究小波、经验模态分解、变模态分解等常见去噪算法,分析了泄漏声波信号的特点,将改进小波阈值去噪和变模态分解去噪相结合,提出了变模态分解-小波变换(VMD-Wavelet:Variable Mode Decomposition-Wavelet)联合去噪算法。利用该算法对典型信号进行去噪运算仿真,结果表明,该联合去噪算法性能优于常见算法。最后,将VMD-Wavelet联合去噪算法应用于实际采集的油气管道泄漏声波信号去噪处理,研究发现,该去噪算法对强背景噪声下的泄漏声波信号能取得很高的信噪比改善和很小的均方误差。

Intelligently Tuned Wavelet Parameters for GPS/INS Error Estimation

This paper presents a new algorithm for de-noising global positioning system （GPS） and inertial navigation system （INS） data and estimates the INS error using wavelet multi-resolution analysis algorithm （WMRA）-based genetic algorithm （GA） with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of the most exploited techniques that have been recently used to increase the precision and reliability of the integrated GPS/INS navigation system. To ameliorate the WMRA algorithm, GA was exploited to optimize the wavelet parameters so as to determine the best wavelet filter, thresholding selection rule （TSR）, and the optimum level of decomposition （LOD）. This results in increasing the robustness of the WMRA algorithm to estimate the INS error. The proposed intelligent technique has overcome the drawbacks of the tedious selection for WMRA algorithm parameters. Finally, the proposed method improved the stability and reliability of the estimated INS error using real field test data.

Neutron-gamma discrimination method based on blind source separation and machine learning

The discrimination of neutrons from gamma rays in a mixed radiation field is crucial in neutron detection tasks.Several approaches have been proposed to enhance the performance and accuracy of neutron-gamma discrimination.However,their performances are often associated with certain factors,such as experimental requirements and resulting mixed signals.The main purpose of this study is to achieve fast and accurate neutron-gamma discrimination without a priori information on the signal to be analyzed,as well as the experimental setup.Here,a novel method is proposed based on two concepts.The first method exploits the power of nonnegative tensor factorization(NTF)as a blind source separation method to extract the original components from the mixture signals recorded at the output of the stilbene scintillator detector.The second one is based on the principles of support vector machine(SVM)to identify and discriminate these components.In addition to these two main methods,we adopted the Mexican-hat function as a continuous wavelet transform to characterize the components extracted using the NTF model.The resulting scalograms are processed as colored images,which are segmented into two distinct classes using the Otsu thresholding method to extract the features of interest of the neutrons and gamma-ray components from the background noise.We subsequently used principal component analysis to select the most significant of these features wich are used in the training and testing datasets for SVM.Bias-variance analysis is used to optimize the SVM model by finding the optimal level of model complexity with the highest possible generalization performance.In this framework,the obtained results have verified a suitable bias–variance trade-off value.We achieved an operational SVM prediction model for neutron-gamma classification with a high true-positive rate.The accuracy and performance of the SVM based on the NTF was evaluated and validated by comparing it to the charge comparison method via figure of merit.The results indicate that the proposed approach has a superior discrimination quality(figure of merit of 2.20).

WAVELET-BASED FAIRING OF B-SPLINE SURFACES

A method of fairing B spline surfaces by wavelet decomposition is investigated. The wavelet decomposition and reconstruction of quasi uniform bicubic B spline surfaces are described in detail. A method is introduced to approximate a B spline surface by a quasi uniform one. An error control approach for wavelet based fairing is suggested. Samples are given to show the feasibility of the algorithms presented in this paper. The practice showed that the wavelet based fairing is better than energy based one in case where the number of vertices of the B spline surface is greater than 1000. The quantitative variance of the approximation error in accordance with the change of decomposition levels needs to be further explored.

Vibration Measurement of Pedestrian Bridge Using Double Magnetic Suspension Vibrator Based on Wavelet Analysis

Aiming at the problem of pedestrian bridge vibration measurement,a vibration measurement system of pedestrian bridge with dual magnetic suspension vibrator structure was designed according to absolute vibration measurement principle. The relationship between the magnetic repulsion force of vibrator and its displacement was obtained by the experimental method and the least square fitting method. The vibration equations of two magnetic suspension vibrators were deduced respectively,and the measurement sensitivity of the system was deduced. The amplitude-frequency characteristic of the system was studied. A simulation model of vibrator measurement system with double magnetic suspension vibrator was established. The analysis shows that the sensitivity of the vibration measurement system with double magnetic suspension vibrator is higher than that with single magnetic suspension vibrator. The four vibration waveforms were measured,that is,no one passes through a pedestrian bridge,there are cars running under the pedestrian bridge,single pedestrian passes through the pedestrian bridge and multiple pedestrians pass through the pedestrian bridge. The multi-scale one-dimensional wavelet decomposition function was used to analyze the vibration signals. The vibration characteristics were obtained using one dimension wavelet decomposition function under four different conditions. Finally,the vibration waveforms of four cases were reconstructed. The measured results show that the vibration measurement system of pedestrian bridge with double magnetic suspension vibrator structure has high measurement sensitivity. The design has a certain value to monitor a pedestrian bridge.

基于POA-VMD-WT的MEMS去噪方法

针对MEMS传感器所测得的加速度和角速度输出信号噪声较大问题,提出一种基于鹈鹕优化算法(pelican optimization algorithm,POA)的变分模态分解(variational mode decomposition,VMD)结合小波阈值(wavelet threshold,WT)的去噪方法。首先利用POA对VMD的参数组合进行优化选择,然后应用POA-VMD将含噪信号自适应、非递归地分解为一系列本征模态函数(intrinsic mode function,IMF)。再通过计算每个IMF的余弦相似度对IMFs进行分类,根据计算结果将IMFs分为噪声主导分量与信号主导分量,对分类后的噪声主导分量进行改进小波阈值去噪处理,最后对处理后的噪声分量与信号主导分量进行重构,获得降噪后的MEMS传感器信号。静态和动态实验结果表明,该方法去噪处理后信号的信噪比分别提高12和10 dB,均方误差分别降低75.5%和46.6%,去噪效果显著,能够提高MEMS传感器的精度。

基于曲面控制点重构的加工误差在机测量方法

为了提高汽车车身外覆盖件模具的加工精度,提出一种在机测量自由曲面加工误差的方法。首先基于改进波前法生成三角网格,提取理论曲面网格节点的坐标数据,根据在机测量得到实际加工曲面的采样数据;然后基于T-splines的小波控制点法进行曲面重构,拟合加工曲面;最后基于广义牛顿法计算重构的实际曲面控制点到理论曲面的法向距离,获得曲面的加工误差分布,并对实验加工的凹凸曲面样件的轮廓度误差进行分析。实验结果表明,基于T-splines控制点法的曲面重构方法能够在机、有效地获得自由曲面的加工误差。

新小波阈值法与VMD相结合的滚动轴承特征提取

针对滚动轴承故障信号弱以及难提取等问题,提出了一种新小波阈值方法与VMD相结合的轴承故障信号特征提取方法。首先,利用一种改进的指数小波阈值函数来优化传统小波降噪方法,克服其存在间断点和恒定偏差等问题;然后,结合VMD提取滚动轴承的有效故障特征;最后,以6205-RS号轴承内圈故障数据作为原始信号进行实验验证。实验结果表明,该方法能够有效提高降噪信号的信噪比,降低均方根误差,保证滚动轴承微弱故障信号特征提取的完整性和有效性。

次同步振荡在交直流电网中传播的关键影响因素

随着“双高”电力系统的发展,次同步振荡问题日益凸出,亟需研究交直流线路次同步振荡传播的关键影响因素。从系统响应量测时序数据着手,提出了一种次同步振荡传播关键影响因素定量分析方法。首先,基于自适应噪声完全集合经验模态分解(complete ensemble empirical mode decomposition, CEEMDAN)的改进小波阈值去噪方法对量测数据进行降噪处理,减少噪声对Prony分析的影响;其次,基于次同步振荡传播各影响因素的相关系数和互信息量建立相关性评价组合模型;最后,计算交直流不同参数在综合模型中的评价指标,得出次同步振荡在交直流线路中传播的关键影响因素。通过在PSCAD搭建2区域4机系统进行分析,结果表明:影响交流线路次同步振荡传播的极强相关参数为交流线路潮流,影响直流线路次同步振荡传播的极强相关参数为次同步振荡频率下交流线路阻抗特性。

CEEMDAN-SE-WT降噪方法在航空发动机燃油流量信号中的应用

燃油流量信号是反映发动机状态和计算飞机排放物排放量的重要信号,但飞机飞行过程中传感器采集信号时不可避免地会受到外界环境以及内部因素干扰。提出一种结合样本熵(sample entropy,SE)的完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)与小波变换(wavelet transform,WT)的联合降噪方法。首先使用CEEMDAN对燃油流量信号进行分解得到本征模态分量,利用样本熵筛选含噪分量,并用相关系数与方差贡献率进行复核。对于含噪分量使用小波阈值降噪进行处理。最后将未处理的模态分量和完成降噪的模态分量重构得到最终燃油流量信号。通过与其他方法比较,CEEMDAN-SE-WT方法拥有最高信噪比为85.287,降噪后燃油消耗总量与飞机总重变化最为接近,可以认为该方法较大程度保留了燃油流量信号中的有效特征,为后续计算民机排放物排放总量提供了良好的数据支持。

基于数据挖掘的大学生就业指导资源挖掘方法

采用目前方法对大学生就业指导资源进行数据挖掘时,由于去噪性能差导致方法存在数据冗余、挖掘效率低和精准度较差的问题,因此提出了基于数据挖掘的大学生就业指导资源挖掘方法。利用H-BIRCH算法对就业指导数据进行聚类处理,结合EMD分解方法和小波去噪方法对不同类别的就业信息数据进行去噪,对去噪后的资源数据进行白化处理,采用图模型提取就业指导资源数据特征,完成就业指导资源挖掘。实验结果表明,该方法可以有效简化数据结构,数据冗余纠错率、数据挖掘效率和数据挖掘准确度较高。

基于参数优化VMD-小波阈值的轴承振动信号降噪方法

为了解决复杂工况下滚动轴承振动信号存在随机噪声的问题,提出了一种基于参数优化变分模态分解(VMD)-小波阈值的滚动轴承降噪方法。首先,利用以包络熵为适应度函数的天鹰算法对变分模态分解算法的模态分解数K和惩罚因子α进行了自适应选择,代入VMD分解中,得到若干本征模态函数(IMFs);然后,根据峭度-相关系数将IMF分量划分为纯净分量和含噪分量,对含噪分量进行了小波阈值降噪处理;最后,对处理后的分量进行了重构,并用重构信号进行了包络谱分析,实现了滚动轴承的信号降噪目的,并利用仿真信号和美国凯斯西储大学公开的轴承数据集对上述降噪方法的有效性进行了验证。研究结果表明:基于参数优化VMD-小波阈值的降噪方法减少了滚动轴承运行状态下的随机噪声,相对小波阈值降噪方法,所得仿真信号信噪比提升53%,均方误差降低13%;在故障特征频率为162 Hz时,所得实验降噪信号包络谱的前6倍频谱峰值更为明显,且受随机噪声影响较小。该研究方法在滚动轴承等旋转机械信号降噪方面具有一定的参考价值。

地震子波分解与重构技术在薄储层预测中的应用

T813K-TK715井区是塔河油田的重点含油气井区,多口井在石炭系油气显示活跃,但砂体展布不明。在地震资料上,T813K-TK715井区储层响应特征不明确,且石炭系卡拉沙依组不整合面强反射屏蔽下伏储层反射信息,使得剥蚀线附近地层圈闭难以刻画。文中结合工区地质特征,建立二维地震和楔状地层模型,应用正演模拟技术得到正演模拟结果,利用子波分解与重构技术对正演模拟结果进行处理。结果表明:子波分解与重构技术有效压制了石炭系卡拉沙依组不整合面强反射,削弱了其对下伏砂体反射的屏蔽作用,增强了地层底部反射的连续性,并且提高了地震剖面识别尖灭点的精度,为塔河油田T813K-TK715井区识别强反射屏蔽背景下的薄砂体及地层尖灭点提供了新思路及技术支撑。