共查询到17条相似文献，搜索用时 140 毫秒
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top-hat变换与庐枞矿集区大地电磁强干扰分离 总被引：1，自引：0，他引：1
作为一种非线性信号处理方法,基于数学形态学的广义形态滤波已经展现出其在大地电磁时间域信号去噪中的作用;然而,广义形态滤波在滤除大地电磁时间域信号中噪声波形的同时,也滤除了时间域信号中包含有用信息的缓变化。针对这一问题,提出一种基于数学形态学top-hat变换的大地电磁时间域噪声压制方案,利用top-hat变换对波峰和波谷的检测能力,采用直线型结构元素,对大地电磁时间域信号进行去噪。用该方法对庐枞矿集区大地电磁实测数据进行处理后,数据的标准差与曲线相似性参数都优于处理前数据,表明所提方法能够去除噪声波形并保留时间域信号的缓变化,恢复受噪声污染的大地电磁时间域信号,提高大地电磁视电阻率曲线的质量。 相似文献
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基于频率域小波去噪的大地电磁信号工频干扰处理 总被引：1，自引：0，他引：1
大地电磁测深(Magnetotelluric,MT)在油气勘查中得到越来越多的应用,针对MT中日益严重的工频干扰,从频率域着手,结合小波阈值去噪方法,提出了基于频率域小波去噪的MT信号工频干扰处理方法。先对受噪的MT信号进行傅里叶变换,得到其实部和虚部,再用小波阈值去噪的方法对实部序列和虚部序列分别进行去噪处理,最后将去噪后的实部和虚部联合,进行反傅里叶变换得到去噪后的信号。给出了去噪方法的原理、步骤,并用仿真信号和实测大地电磁信号验证了其有效性。结果表明:频率域小波阈值去噪的大地电磁信号工频干扰处理方法是正确、有效的,能有效且自适应地压制大地电磁信号中的工频干扰,突出被工频干扰淹没了的有用信号的信息。 相似文献
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针对油气勘探中大地电磁(MT)数据易受各类干扰的污染,且信噪难以分离的问题,把基于广义S变换的时频滤波技术应用于MT数据处理中来,得到MT数据的S域时频分布,分析受噪MT数据在S域的时频分布特征,再在S变换时频域进行时频阈值去噪,并对滤波后的S域时频谱进行逆变换重构,分离得到去噪后的MT数据。给出了基于广义S域时频滤波的方法原理与应用步骤,对被污染的仿真和实测MT数据进行了时频阈值滤波,并与小波阈值去噪方法进行了比较研究。结果表明：基于广义S变换的时频滤波方法可有效抑制MT数据中的干扰,从噪声信号中分离出有效的大地电磁数据,且减少了人为参与,提高了MT勘测的数据质量。 相似文献
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A combinatorial filtering method for magnetotelluric data series with strong interference 总被引：1，自引：0，他引：1
Cai Jian-hua 《Arabian Journal of Geosciences》2016,9(13):628
In highly industrialized areas, magnetotelluric (MT)-induced variations are contaminated by strong manmade noise signals. A method is described as an alternative approach for noise removal, based on a combination of empirical mode decomposition (EMD) with independent component analysis (ICA). The filtering procedure takes advantage of the fact that data are analyzed through different scale levels, which requires a minimum of human intervention and leaves good data sections unchanged. Principle and steps of method are discussed, and de-noising results are evaluated by some parameters. After the filtering stage, data is processed in the frequency domain to yield two sets of reliable MT transfer functions and the result was compared with that of the EMD-Wavelet method. Simulated signal and measured MT data series are processed. The results show that this procedure can lead to greatly improved apparent resistivity and phase curves after processing. Point defects are filtered out to eliminate their deleterious influence, which yields reliable estimates of the MT transfer functions. The EMD-ICA method provides a new method for the de-noising of MT data series under the condition of low SNR. 相似文献
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Wavelet de-noising method is often used in the processing of magnetotelluric (MT) signal, and the wavelet hard and soft threshold de-noising method are the most popular although there is much room for improvement in the threshold function selection and threshold determination. A new adaptive wavelet threshold de-noising method was proposed by selecting a new threshold function and presenting an adaptive method for obtaining optimal threshold based on the multi-resolution Stein unbiased risk estimation. New threshold function and an adaptive method to determine threshold were discussed, and the principle and implementation of the algorithm were given. The simulated signal and the measured MT data contaminated by impulse interference were analyzed, and the obtained results were compared with those of the conventional wavelet hard and soft threshold de-noising methods. The results show that the proposed method overcomes the defects of the traditional wavelet soft and hard threshold due to a new threshold function, and a new method to determine the threshold of each layer is applied and provides an adaptive method for filtering MT data in the wavelet domain that requires a minimum of human intervention. The presented de-noising method is very suitable for suppressing the impulse interference for MT data and can get higher signal-to-noise ratio than the traditional wavelet threshold de-noising methods. After de-noising, the accurate data is loaded for further impedance estimation and geological interpretation. 相似文献
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核磁共振测井中采集到的回波串信号十分微弱,而背景噪声很强,使信噪分离困难。为解决这一问题,引入了结合数学形态学的特征识别和小波分解的多分辨率分析特性的形态小波方法。讨论了方法的数据基础和应用步骤,并与小波软阈值方法处理结果进行了对比分析。实测数据处理结果表明:形态小波去噪方法具有良好的细节保留和抗噪声能力,去噪效果优于小波软阈值滤波方法;在消除测井信号随机噪声的同时,能很好地保留信号的波形和特征,在较低信噪比下仍可有效地提取测井信号的有用信息,提高了T2谱的反演精度。 相似文献
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广义S变换时频域滤波在MT数据处理中的应用 总被引：1，自引：0，他引：1
短时傅里叶变换是建立在稳态信号基础之上,它仅能提供信号的频域信息,对信号的时间分辨能力差。这影响了它在大地电磁测深数据处理中的应用效果。S变换是一种优于短时傅立叶变换的时频分析方法,能够提供信号时-频域信息。利用S变换对大地电磁测深数据进行时频分析,有助于实现大地电磁测深数据噪声的时频-域滤波,从而提高大地电、磁分量数据的频谱分析精度。从广义S变换理论出发,分析了各类波形噪声的时-频域特征及其对大地电磁测深数据的影响。针对大地电磁测深数据处理特点,利用广义S变换得到时频谱,采用时频比值和门槛值方法,研究适合压制电磁噪声的时频滤波器和滤波方法。对实际大地电磁测深数据的处理结果表明这个方法提高了阻抗张量的估算质量,验证了该方法的有效性。 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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Factorial Kriging (FK) is a data- dependent spatial filtering method that can be used to remove both independent and correlated
noise on geological images as well as to enhance lineaments for subsequent geological interpretation. The spatial variability
of signal, noise, and lineaments, characterized by a variogram model, have been used explicitly in calculating FK filter coefficients
that are equivalent to the kriging weighting coefficients. This is in contrast to the conventional spatial filtering method
by predefined, data-independent filters, such as Gaussian and Sobel filters. The geostatistically optimal FK filter coefficients,
however, do not guarantee an optimal filtering effect, if filter geometry (size and shape) are not properly selected. The
selection of filter geometry has been investigated by examining the sensitivity of the FK filter coefficients to changes in
filter size as well as variogram characteristics, such as nugget effect, type, range of influence, and anisotropy. The efficiency
of data-dependent FK filtering relative to data-independent spatial filters has been evaluated through simulated stochastic
images by two examples. In the first example, both FK and data-independent filters are used to remove white noise in simulated
images. FK filtering results in a less blurring effect than the data-independent fillers, even for a filter size as large
as 9 × 9. In the second example, FK and data-independent filters are compared relative to the extraction of lineaments and
components showing anisotropic variability. It was determined that square windows of the filter mask are effective only for
removing Isotropie components or white noise. A nonsquare windows must be used if anisotropic components are to be filtered
out. FK filtering for lineament enhancement is shown to be resistant to image noise, whereas data-independent filters are
sensitive to the presence of noise. We also have applied the FK filtering to the GLORIA side-scan sonar image from the Gulf
of Mexico, illustrating that FK is superior to the data-independent filters in removing noise and enhancing lineaments. The
case study also demonstrate that variogram analysis and FK filtering can be used for large images if a spectral analysis and
optimal filter design in the frequency domain is prohibitive because of a large memory requirement. 相似文献
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《Comptes Rendus Geoscience》2015,347(1):2-12
Time-frequency peak filtering (TFPF) is an effective method for seismic random noise attenuation. The linearity of the signal has a significant influence on the accuracy of the TFPF method. The higher the linearity of the signal to be filtered is, the better the denoising result is. With this in mind, and taking the lateral coherence of reflected events into account, we do TFPF along the reflected events to improve the degree of linearity and enhance the continuity of these events. The key factor to realize this idea is to find the traces of the reflected events. However, the traces of the events are too hard to obtain in the complicated field seismic data. In this paper, we propose a Multiple Directional TFPF (MD–TFPF), in which the filtering is performed in certain direction components of the seismic data. These components are obtained by a directional filter bank. In each direction component, we do TFPF along these decomposed reflected events (the local direction of the events) instead of the channel direction. The final result is achieved by adding up the filtering results of all decomposition directions of seismic data. In this way, filtering along the reflected events is implemented without accurately finding the directions. The effectiveness of the proposed method is tested on synthetic and field seismic data. The experimental results demonstrate that MD–TFPF can more effectively eliminate random noise and enhance the continuity of the reflected events with better preservation than the conventional TFPF, curvelet denoising method and F–X deconvolution method. 相似文献