共查询到15条相似文献，搜索用时 125 毫秒
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针对曲波阈值去噪方法阈值选取单一造成的有效信号损失或随机噪声压制不完全的问题,笔者提出了一种基于经验模态分解的曲波阈值去噪方法。该方法首先对带噪信号进行经验模态分解得到一系列固有模态函数,根据每个固有模态函数所含噪声强弱的不同,选取不同的阈值分别对分解得到的含噪固有模态函数进行曲波阈值降噪处理;最后将去噪后的固有模态函数与不含噪声的固有模态函数进行信号重构得到最终压噪的结果。由于引入经验模态分解,对分解得到的不同含噪程度的固有模态函数,选取不同的阈值进行处理,这样能够有效减小直接曲波阈值方法阈值选取单一产生的问题。模型和实际数据试算表明,该方法在提高数据信噪比的同时,能够有效地保留有效信号,是一种相对保幅的去噪方法。 相似文献
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基于整体经验模态分解和支持向量回归的北京市 PM 2．5预测 总被引：5，自引：0，他引：5
为了更好地掌握大气中PM2.5浓度的变化规律,利用EEMD-SVR混合模型对该地区的PM2.5浓度值进行了短期预测。首先,通过采用整体经验模态分解(EEMD)方法分析北京市PM2.5,把原始时间序列分解成多个固有模态函数和趋势项;然后,对各阶固有模态函数进行周期性分析,揭示了北京市PM2.5的周期性变化特点;最后,对经过EEMD分解后的各阶固有模态函数和趋势项用支持向量机回归(SVR)方法进行预测。结果表明,EEMD-SVR混合模型比单一的SVR模型预测精度更高。 相似文献
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在分析地震资料时,因吸收和衰减等原因,地震信号往往呈现出非稳态性。时频分解方法能将地震信号分解成多个稳态的子成分,从而为描述和分析地震信号的属性提供了便利,如短时傅里叶变换、小波分析、经验模式分解(EMD)等方法。本文引入了一种新的时频分析方法——局部均值分解(LMD)。该方法将地震信号按其时频属性分解成多个乘积分量信号(PFs),较EMD分解所得的固有模态函数(IMFs)保留了更多的局部信息,同时模态混叠效应更少。对模型数据和实际数据的处理结果验证了LMD方法能够合理地分解地震信号,更准确地描述地震资料中不同时间尺度的构造信息,为进一步的地震数据处理和解释提供参考。 相似文献
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裂缝性油气藏越来越受到人们的重视,如何有效地识别和评价裂缝性地层也成为人们关注的一个焦点。Cohen类时频分析方法具有同时对信号的时间和频率进行分析的优点,同时Hilbert-Huang变换(HHT)中的经验模态分解（EMD）方法能将信号分解为有限个具有单分量特性的固有模态函数（IMF）,可以满足Cohen类时频分析对信号单分量的要求。尝试将这两种方法联合应用于阵列声波信号特性的提取,对地层不同性质裂缝的信号能量的时频分布特征进行分析,结果显示了这种联合时频分析方法在不同的裂缝性质地层中有不同的表现特征,对于实现裂缝性地层分析和评价具有一定的现实意义。 相似文献
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Potential field data is generally contaminated by random noise. The high-frequency noise contained in the data brings unfavorable influences to subsequent data processing. Therefore, suppressing the adverse effects of noise has always been a crucial step which is desirable prior to applying other transformations. Over the past decades, numerous mathematical approaches have been proposed for noise cancelation of potential field data. In the work discussed in this paper, the application of the empirical mode decomposition for denoising of potential field data is briefly described, and a new stopping criterion for this filtering method is introduced. Using the proposed method, the empirical mode decomposition is firstly performed on the original potential field data to get numerous intrinsic mode functions corresponding to components with different frequencies. Each intrinsic mode function is subtracted from the original data to get different residual datasets. The correlation coefficients associated with the original data and various residual datasets are calculated and plotted. The inflection point of the correlation coefficient curve is adopted as the last intrinsic mode function to be selected. The new stopping criterion offers a quantitative way to determine which intrinsic mode functions should be removed during filtering and can be easily implemented within the algorithm. Tests on synthetic noisy gravity data demonstrate that the empirical mode decomposition based noise cancelation method along with this new stopping criterion yield acceptable filtering results for potential field data. The newly developed method is also investigated on real gravity data collected over a magnetite zone in Jilin Province, China. 相似文献
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电磁脉冲干扰是大地电磁测深系统(MT)信号的主要噪声之一,严重影响后续视电阻率和阻抗的计算及目标信息的提取。针对脉冲类噪声在时间域中的变化特征,利用经验模态分解（EMD）对脉冲类电磁噪声进行压制处理。首先,对大地电磁信号经EMD分解后得到N个本征模态函数（IMF）;然后,对每一阶的IMF选择一个合适的阀值,对于该IMF中超出该阀值的部分进行截断;最后,进行EMD重构。实测数据测试表明:改正后信号能量损失小, 与改正前信号相关性高, 可有效地抑制脉冲类噪声干扰。 相似文献
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水工勘测中物探观测信号的处理及解译 总被引：2，自引：0，他引：2
针对电磁法物探观测信号离散傅立叶变换中信号畸变问题，研究了不同尺度窗口对信号分析的作用和影响。研究表明，不同尺度窗口表现了信号的不同特点，并各自具有一定的优缺点。信号解译中首先应进行小波分析，剔除病态信号，再根据信号和勘测目的，选用不同尺度窗口函数，从而准确、有效地进行信号处理与解译。 相似文献
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《Mathematical Geology》1995,27(6):731-748
Laser Doppler Anemometry (LDA) has proved a powerful tool for quantifying fluid turbulence and is increasingly being applied
in fields such as fluvial sedimentology and geomorphology. When operated in the burst-signal processing mode, high-frequency
velocity fluctuations are measured at irregular time internals. In many situations, there is a need to transform these data
to obtain evenly spaced velocity values but at a lower frequency. However, clear guidelines for this type of data processing
are lacking. Three steps are necessary in order to transform the original files into evenly spaced data: (1) resampling at
the average sampling rate, (2) low-pass filtering with half-power frequency adjusted to the final sampling frequency, and
(3) decimating at the desired frequency. The decision taken at each step will affect the resulting signal and may cause, if
not assessed carefully, severe problems in the signal such as aliasing errors. This paper examines each stage of data processing
and details the advantages and drawbacks of different techniques in relation to the effects on turbulence statistics (variance,
instantaneous shear stress, etc.). A standard method and specific guidelines are finally proposed.
An erratum to this article is available at . 相似文献
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基于经验模态分解(EMD)的小波熵阈值地震信号去噪 总被引：1，自引：0，他引：1
针对EMD阈值去噪算法中阈值由经验选取以及无法有效区分各固有模态函数上有用信息的不足,本文对各固有模态函数进行小波变换,对各层小波系数进行相关处理,以突出有效信息,抑制噪声;将细节系数的有效信号和突变点置零并等分为若干区间,选取小波熵最大子区间的高频小波系数平均值作为噪声方差计算得到阈值。该阈值选取方法依据小波熵的特点,自适应地根据对应尺度上信号自身的能量特征确定该尺度阈值。将该算法应用于仿真信号和实际地震信号去噪,结果表明该方法优于基于EMD的小波阈值去噪,在提高去噪效果的同时,也更好地保护有效信号。 相似文献