Improving the resolution of seismic traces based on the secondary time-frequency spectrum

The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time-frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform （ST） and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time frequency spectrum. Second, using the secondary time frequency spectrum, we design a two- dimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time- fi＇equency-space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio （SNR）.

Time-frequency response spectrum of rotational ground motion and its application

The rotational seismic motions are estimated from one station records of the 1999 Jiji （Chi-Chi）, Taiwan, earthquake based on the theory of elastic plane wave propagation. The time-frequency response spectrum （TFRS） of the rotational motions is calculated and its characteristics are analyzed, then the TFRS is applied to analyze the damage mechanism of one twelve-storey frame concrete structure. The results show that one of the ground motion components can not reflect the characteristics of the seismic motions completely; the characteristics of each component, especially rotational motions, need to be studied. The damage line of the structure and TFRS of ground motion are important for seismic design, only the TFRS of input seismic wave is suitable, the structure design is reliable.

Detection method of forward-scatter signal based on Rényi entropy

The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.

Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.

Research on a deconvolution algorithm for laser-induced fluorescence diagnosis based on the maximum entropy principle

Laser-induced fluorescence(LIF)spectroscopy is employed for plasma diagnosis,necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal.However,direct deconvolution becomes invalid in the presence of noise as it leads to infinite amplification of high-frequency noise components.To address this issue,we propose a deconvolution algorithm based on the maximum entropy principle.We validate the effectiveness of the proposed algorithm by utilizing simulated LIF spectra at various noise levels(signal-to-noise ratio,SNR=20–80 d B)and measured LIF spectra with Xe as the working fluid.In the typical measured spectrum(SNR=26.23 d B)experiment,compared with the Gaussian filter and the Richardson–Lucy(R-L)algorithm,the proposed algorithm demonstrates an increase in SNR of 1.39 d B and 4.66 d B,respectively,along with a reduction in the root-meansquare error(RMSE)of 35%and 64%,respectively.Additionally,there is a decrease in the spectral angle(SA)of 0.05 and 0.11,respectively.In the high-quality spectrum(SNR=43.96 d B)experiment,the results show that the running time of the proposed algorithm is reduced by about98%compared with the R-L iterative algorithm.Moreover,the maximum entropy algorithm avoids parameter optimization settings and is more suitable for automatic implementation.In conclusion,the proposed algorithm can accurately resolve Doppler spectrum details while effectively suppressing noise,thus highlighting its advantage in LIF spectral deconvolution applications.

Time-Frequency Entropy Analysis of Arc Signal in Non-Stationary Submerged Arc Welding

The use of time-frequency entropy to quantitatively assess the stability of submerged arc welding process considering the distribution features of arc energy is reported in this paper. Time-frequency entropy is employed to calculate and analyze the stationary current signals, non-stationary current and voltage signals in the submerged arc welding process. It is obtained that time-frequency entropy of arc signal can be used as arc stability judgment criteria of submerged arc welding. Experimental results are provided to confirm the effectiveness of this approach.

Time-Frequency Signal Processing for Gas-Liquid Two Phase Flow Through a Horizontal Venturi Based on Adaptive Optimal-Kernel Theory

A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal of gas-liquid two-phase flow was preprocessed,and then the AOK theory was used to analyze the dynamic differ-ential pressure signal.The mechanism of two-phase flow was discussed through the time-frequency spectrum.On the condition of steady water flow rate,with the increasing of gas flow rate,the flow pattern changes from bubbly flow to slug flow,then to plug flow,meanwhile,the energy distribution of signal fluctuations show significant change that energy transfer from 15-35 Hz band to 0-8 Hz band;moreover,when the flow pattern is slug flow,there are two wave peaks showed in the time-frequency spectrum.Finally,a number of characteristic variables were defined by using the time-frequency spectrum and the ridge of AOK.When the characteristic variables were visu-ally analyzed,the relationship between different combination of characteristic variables and flow patterns would be gotten.The results show that,this method can explain the law of flow in different flow patterns.And characteristic variables,defined by this method,can get a clear description of the flow information.This method provides a new way for the flow pattern identification,and the percentage of correct prediction is up to 91.11%.

Ultrasonic attenuation estimation based on time-frequency analysis

The quality factor(or Q value)is an important parameter for characterizing the inelastic properties of rock.Achieving a Q value estimation with high accuracy and stability is still challenging.In this study,a new method for estimating ultrasonic attenuation using a spectral ratio based on an S transform(SR-ST)is presented to improve the stability and accuracy of Q estimation.The variable window of ST is used to solve the time window problem.We add two window factors to the Gaussian window function in the ST.The window factors can adjust the scale of the Gaussian window function to the ultrasonic signal,which reduces the calculation error attributed to the conventional Gaussian window function.Meanwhile,the frequency bandwidth selection rules for the linear regression of the amplitude ratio are given to further improve stability and accuracy.First,the feasibility and influencing factors of the SR-ST method are studied through numerical testing and standard sample experiments.Second,artificial samples with different Q values are used to study the adaptability and stability of the SR-ST method.Finally,a further comparison between the new method and the conventional spectral ratio method(SR)is conducted using rock field samples,again addressing stability and accuracy.The experimental results show that this method will yield an error of approximately 36%using the conventional Gaussian window function.This problem can be solved by adding the time window factors to the Gaussian window function.The frequency bandwidth selection rules and mean slope value of the amplitude ratio used in the SR-ST method can ensure that the maximum error of different Q values estimation(Q>15)is less than 10%.

Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy

We present a hybrid singular spectrum analysis （SSA） and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the noisy time series into its constituent components including both the deterministic behavior and noise, while fuzzy entropy automatically differentiates the optimal dominant components from the noise based on the complexity of each component. We demonstrate the effectiveness of the hybrid approach in reconstructing the Lorenz and Mackey--Class attractors, as well as improving the multi-step prediction quality of these two series in noisy environments.

Wavelet packet decomposition entropy threshold method for discrete spectrum interferences rejection of on-line partial discharge monitoring

The frequency domain division theory of dyadic wavelet decomposition and wavelet packet decomposition (WPD) with orthogonal wavelet base frame are presented. The WPD coefficients of signals are treated as the outputs of equivalent bandwidth filters with different center frequency. The corresponding WPD entropy values of coefficients increase sharply when the discrete spectrum interferences (DSIs), frequency spectrum of which is centered at several frequency points existing in some frequency region. Based on WPD, an entropy threshold method (ETM) is put forward, in which entropy is used to determine whether partial discharge (PD) signals are interfered by DSIs. Simulation and real data processing demonstrate that ETM works with good efficiency, without pre-knowing DSI information. ETM extracts the phase of PD pulses accurately and can calibrate the quantity of single type discharge.

SAR target detection based on the optimal fractional Gabor spectrum feature

In this paper,an algorithm based on a fractional time-frequency spectrum feature is proposed to improve the accuracy of synthetic aperture radar(SAR)target detection.By extending the fractional Gabor transform(FrGT)into two dimensions,the fractional time-frequency spectrum feature of an image can be obtained.In the achievement process,we search for the optimal order and design the optimal window function to accomplish the two-dimensional optimal FrGT.Finally,the energy attenuation gradient(EAG)feature of the optimal time-frequency spectrum is extracted for high-frequency detection.The simulation results show the proposed algorithm has a good performance in SAR target detection and lays the foundation for recognition.

Harmonicity Spectrum

Perceiving harmonic information (especially weak harmonic information) in time series has important scientific and engineering significance. Fourier spectrum and time-frequency spectrum are commonly used tools for perceiving harmonic information, but they are often ineffective in perceiving weak harmonic signals because they are based on energy or amplitude analysis. Based on the theory of Normal time-frequency transform (NTFT) and complex correlation coefficient, a new type of spectrum, the Harmonicity Spectrum (HS), is developed to perceive harmonic information in time series. HS is based on the degree of signal harmony rather than energy or amplitude analysis, and can therefore perceive very weak harmonic information in signals sensitively. Simulation examples show that HS can detect harmonic information that cannot be detected by Fourier spectrum or time-frequency spectrum. Acoustic data analysis shows that HS has better resolution than traditional LOFAR spectrum.

基于双重分解和双向长短时记忆网络的中长期负荷预测模型

针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(singular spectrum analysis,SSA)双重分解的双向长短时记忆网络(bidirectional long and short time memory,BiLSTM)预测模型。首先,采用CEEMDAN对历史负荷进行分解,以得到若干个周期规律更为清晰的子序列;再利用多尺度熵(multiscale entropy,MSE)计算所有子序列的复杂程度,根据不同时间尺度上的样本熵值将相似的子序列重构聚合;然后,利用SSA去噪的功能,对高度复杂的新序列进行二次分解,去除序列中的噪声并提取更为主要的规律,从而进一步提高中长序列预测精度;再将得到的最终一组子序列输入BiLSTM进行预测;最后,考虑到天气、节假日等外部因素对电力负荷的影响,提出了一种误差修正技术。选取了巴拿马某地区的用电负荷进行实验,实验结果表明,经过双重分解可以将均方根误差降低87.4%;预测未来一年的负荷序列时,采用的BiLSTM模型将拟合系数最高提高2.5%;所提出的误差修正技术可将均方根误差降低9.7%。

基于ICEEMDAN和PSO-LSSVM的石油机械滚动轴承故障诊断方法研究

针对滚动轴承疲劳故障振动信号具有能量弱、特征稀疏等特点,提出了一种通过改进自适应噪声完备经验模态分解方法与粒子群优化的最小二乘支持向量机结合的故障识别方法;对轴承不同故障信号利用改进的自适应噪声完备经验模态算法分解为一系列固有模态函数分量;根据相关系数-方差贡献率准则筛选出最能表征原始信号状态的分量,并计算重构分量的奇异谱熵值构成特征向量;将提取的特征向量集合输入到基于粒子群优化的最小二乘支持向量机分类器中,进行模型的训练和故障模式的识别,与SVM和LSSVM分类器模型进行准确率和效率比较;试验结果表明,该方法在滚动轴承故障信号中能有效提取故障特征,准确率达98.75%,具有一定可靠性和实用性。

基于功率谱熵的无线电引信目标与干扰信号分类方法

无线电调频引信在战场环境容易受到干扰信号的干扰导致早炸,丧失打击能力。为提升无线电调频引信抗干扰能力,准确识别引信目标与干扰信号,提出一种基于功率谱熵特征的无线电调频引信目标与干扰信号分类识别方法。利用实测采集的无线电引信检波端输出信号,通过提取目标和干扰信号的功率谱指数熵和Renyi熵特征构成特征向量,作为K邻近(KNN)分类器的输入进行目标和干扰信号分类识别,并利用5-折交叉检验方法对其进行验证。结果表明:目标和干扰信号的功率指数熵和Renyi熵具有显著差异性,使用KNN分类器对其进行分类识别时,最高的识别准确率可达99.47%。

一种基于类小波变换的无线电频谱监测数据无损压缩方法

无线电频谱监测海量数据存储和分析是无线电监管工作的重要组成部分。频谱数据具有时间相关性以及不同频点间的相关冗余,对此本文设计了一种基于类小波变换的无线电频谱监测数据无损压缩方法。该方法首先基于时间相关性将一维频谱信号转换成二维矩阵;转换成二维矩阵后数据在水平方向以及垂直方向都存在冗余,算法采用卷积神经网络来代替传统小波中的预测和更新模块,并引入了自适应压缩块来处理不同维度的特征,从而获得更紧凑的频谱数据表示。研究进一步设计了一种基于上下文的深度熵模型,利用类小波变换不同子带系数获得熵编码参数,以此估计累积概率,从而实现频谱数据的压缩。实验结果表明,与已有的Deflate等传统频谱监测数据无损压缩方法相比,本文算法有进一步的性能提升,与典型的JPEG2000、PNG、JPEG-LS等二维图像无损压缩方法相比,本文所提出的方法的压缩效果也提高了20%以上。

ITD结合参数优化MOMEDA的滚动轴承故障特征提取

针对固有时间尺度分解(Intrinsic time scale decomposition,ITD)方法在强背景噪声影响下难以提取轴承故障特征的问题,提出了一种ITD与参数优化的多点最优最小熵解卷积(Multipoint optimal minimum entropy deconvolution adjusted,MOMEDA)相结合的滚动轴承故障特征提取方法。首先根据包络谱峰值因子最大原则提取包含丰富故障信息的ITD分量,其次对该分量进行MOMEDA降噪处理。对影响MOMEDA滤波效果的两个参数——故障周期T与滤波器长度L分别以多点峭度和平方包络谱的基尼指数进行优化,最后进行包络谱分析提取故障特征频率。通过仿真信号与实测信号分析表明该方法能在强噪声干扰下有效提取故障特征。

坡谱信息熵与区域水土流失关系研究

坡谱信息熵可以在宏观上反映地形起伏特征和地貌组合差异,且计算相对便捷。为探索坡谱信息熵与区域水土流失之间的关系,文章以西北黄土高原水土保持区为例,基于ALOS数字高程模型(空间分辨率12.5 m)计算研究区内的坡谱信息熵、土壤侵蚀模数,构建坡谱信息熵与土壤侵蚀模数之间的函数关系。研究结果表明:研究区内坡谱曲线主要为“L”形、“S”形,坡谱信息熵值在0.67~1.72nat之间;平均土壤侵蚀模数为1940t/(km^(2)·a)。在流域尺度、二级尺度和三级尺度上,构建坡谱信息熵与土壤侵蚀模数的函数模型,均呈极显著的多项式函数关系,但在流域尺度上,坡谱信息熵与土壤侵蚀模数的关系存在较大不确定性。

基于传感信号采集的电控发动机振动故障监测方法

通过调理振动信号可以更高效地监测振动故障。为此,提出基于传感信号采集的电控发动机振动故障监测方法。首先,搭建电控发电机传感信号采集与处理架构,通过放大传感信号增益、滤波和转换信号模数的方式处理待监测信号,为提高监测准确性奠定可靠的数据基础。通过小波包分解与重构,获取信号的时域参数和小波能谱熵,并构建三维特征量。然后,利用“一对一”分解策略优化孪生支持向量机,构造多元分类器,使其更适用于振动故障监测这一多类别分类问题,再输入待监测信号的特征量,通过确定故障类别实现持续性监测。仿真结果表明:该方法训练耗时的最大值仅为897 ms,对于转子摩擦振动、不平衡振动等5种类型故障的监测准确率始终在97%以上,在缩减训练样本后准确率仍保持在90%以上。

熵平均密度分段压缩感知反射光谱重建方法

反射光谱作为物体表面重要特征被广泛用于远程遥感目标识别、物质成分的含量检测、农业作物成熟度检测、医学影像的疾病诊断等领域。为解决反射光谱数据冗余,实现全光谱数据稀疏表达和提高光谱重建精度,将压缩感知技术应用于光谱数据分析和处理。针对全局光谱压缩感知重建方法中各波段数据稀疏度的差异性对采样率的限制条件不同,提出熵平均密度分段压缩感知反射光谱重建方法。首先定义熵平均密度作为光谱分段参考量来寻找光谱分段断点和判定各分段光谱的熵密度高低。而后依据有限等距约束条件重新分配各分段光谱的采样率,生成测量矩阵和稀疏矩阵完成各局部反射光谱稀疏感知。采用正交匹配追踪算法求最优解,分配各分段光谱的迭代次数,用感知矩阵中的列原子和稀疏信号进行迭代匹配重构各局部反射光谱,将各重构的局部反射光谱缝合为全局反射光谱。用全局光谱压缩感知方法和该方法对标准色块24Munsell ColorChecker的反射光谱进行对比实验。实验结果表明,较之于全局光谱压缩感知方法,该方法重建光谱曲线高熵密度区重建精度更高,低熵密度区压缩效率更高,在总压缩采样率不变的情况下,RMSE和MAPE统计数据得到改善,提升了整体曲线重建效果。