基于分数阶傅里叶变换的混合Cadzow滤波法

常规频率域SVD滤波法在随机噪声压制处理时,容易造成弯曲同相轴损伤。针对这一问题,本文提出一种基于分数阶傅里叶变换的混合Cadzow滤波法压制三维地震数据中随机噪声的方法。首先应用分数阶傅里叶变换,将地震数据变换到时频面,再依据Eigenimage滤波法与Cadzow滤波法建立混合的高维Hankel矩阵,然后对其运行奇异值分解,最后通过秩约化的方法来压制随机噪声。此方法用于四川某地区的地震数据处理,结果表明该方法可有效地去除随机噪声,保护有效信号,提高叠后地震数据的信噪比。     

基于两步SVD变换的零偏VSP资料上下行波场分离方法

垂直地震剖面(Vertical Seismic Profiling,VSP)资料处理中波场分离是关键问题之一.随着属性提取技术的发展,新的属性参数(例如Q值)提取技术对波场分离的保真性要求越来越高.本文改进了传统奇异值分解(Singular Value Decomposition,SVD)法,给出了一种对波场的动力学特征具有更好的保真性,可以作为Q值提取的预处理步骤的零偏VSP资料上下行波场分离方法.该方法通过两步奇异值分解变换实现:第一步,排齐下行波同相轴,利用SVD变换压制部分下行波能量;第二步,在剩余波场中排齐上行波同相轴,使用SVD变换提取上行波场.在该方法的实现过程中,压制部分下行波能量后的剩余波场中仍然存在较强的下行波干扰,使得上行波同相轴的排齐比较困难.本文给出了一种通过极大化多道数据线性相关程度(Maximize Coherence,MC)排齐同相轴的算法,在一定程度上解决了低信噪比下排齐同相轴的问题.将本文提出的方法用于合成数据和实际资料的处理,并与传统SVD法的处理结果进行对比,结果表明本文提出的波场分离方法具有良好的保真性,得到波场的质量明显优于传统SVD法.通过对本文方法和传统SVD法处理合成数据得到的下行波场提取Q值,然后进行对比可知,本文方法可以有效提高所提取Q值的准确性,适合作为Q值提取的预处理步骤.     

基于复数域混合SVD滤波方法及在随机噪声压制中的应用

在低信噪比地震资料处理中,压制随机噪声是其中的关键处理环节.传统的频率空间域预测滤波方法,容易使得去噪后高频段的有效信号严重畸变,不利于进一步提高分辨率.为此,提出了一种基于复数域混合SVD滤波法压制三维地震数据中随机干扰的方法,该方法首先在时空域对地震数据作傅里叶变换,再依据Eigenimage滤波法与Cadzow滤波法建立混合Hankel矩阵,然后对其运行奇异值分解,最后通过秩约化的方法来压制随机干扰.理论模型和实际地震数据的应用表明:该方法可有效地去除随机噪声,保护有效波,明显地改善了叠后三维地震资料的信噪比.     

基于相似度权重算子和f-x域经验模态分解的随机噪声衰减方法（英文）

传统的f-x域经验模态分解法(Empirical mode decomposition,EMD)能够有效地对主要由水平同相轴构成的地震记录进行随机噪声衰减。然而,当同相轴倾斜时,f-x域经验模态分解法在衰减随机噪声的同时去除大部分有效信号。本文提出了一种基于f-x域经验模态分解法的改进算法。我们通过局部相似度对所去除的噪声信号中的有效信号进行提取。局部相似度可以用来检测噪声信号中的有效信号点并用来构造一权重算子进行信号提取。新方法与f-x域经验模态分解法、f-x域预测滤波法以及f-x域经验模态分解预测滤波法相比能够在衰减随机噪声的同时保留更多的有用信号。数值模拟实验以及实际地震资料处理结果均表明该方法能更为有效地去噪。     

电磁法数据处理的奇异值分解法

本文提出用奇异值分解方法处理电磁响应数据.根据电磁响应数据的奇异值分布特征,可以指出有用信号和随机误差所对应的奇异值范围.截断随机误差所对应的奇异值,利用与有用信号对应的特征象重构数据,可以消除随机误差.对加入随机噪声的理论数据处理的结果表明,这种方法的效果明显,随机噪声的标准差可降低60％多.另外,利用不同区段的特征象重构数据,可区分不同级次的电磁响应特征.最后,给出对实测数据处理的结果.     

改进的正交多项式变换在地震资料去噪中的应用

水平叠加虽然在很大程度上压制了噪声,提高了地震剖面的信噪比,但CMP遭集上还存在不少不是一次波的规则干扰和随机噪声,不利于叠前资料的岩性反演和叠后资料的波阻抗反演.本文提出了基于改进的正交多项式变换压制地震资料中随机噪声的方法,其优势在于:通过对不同时间信号的奇异值分解,确定有效信号正交多项式系数谱的阶数;再利用小波变换,改善有效信号和噪声在低阶上的混叠.文中给出了具体处理的过程,数据试验和实际资料的处理结果表明该方法不仅能有效地压制噪声,而且还能较好地保护地震数据中AVO变化特征.     

一种神经网络改进小波的地震数据随机噪声去除方法

地震资料的有效信号反射弱,且易受多次波的影响,不可避免地存在随机噪声干扰。提出一种基于神经网络改进小波的地震数据随机噪声去除方法,采用神经网络模型,识别出随机噪声信号,对该信号进行小波包分解,获取多类别随机噪声信号,采用级联BP神经网络模型提取出多类别随机噪声信号,实现地震数据的随机信号压制。实验结果显示,这种改进小波方法对地震数据随机噪声信号的去噪效果较好,在复杂沉积地质结构被探测介质的地震数据随机噪声压制方面具有较强的适用性。     

SVD地震波场分离与去噪技术在鄂尔多斯盆地地震资料处理中的应用

针对鄂尔多斯盆地地区地震资料自身的特点,本文依据地震信号传播的特性和波场之间的差异,通过多域(多种线性变换域和频域)奇异值分解(SVD),然后提取目标信号的奇异值重构地震信号的方法,实现地震波场分离与去噪处理.与传统SVD地震波场分离与去噪技术相比较,该方法的目的性更强,直接针对感兴趣的地震信号成分进行SVD波场分离与去噪,在提高地震资料信噪比的同时,确保了信号的高保真度和分辨率;同时,避免了以往SVD技术应用空间狭窄,有效信号损失严重等缺陷性.从实际资料处理结果来看,取得了较好的效果.     

基于经验模态分解的分数维地震随机噪声衰减方法

经验模态分解算法(EMD)是一种基于有效波和噪声尺度差异进行波场分离的随机噪声压制方法,但由于实际地震数据波场复杂,导致模态混叠较严重,仅凭该方法进行去噪很难达到理想效果.本文基于EMD算法对信号多尺度的分解特性,结合Hausdorff维数约束条件,提出一种用于地震随机噪声衰减的新方法.首先对地震数据进行EMD自适应分解,得到一系列具有不同尺度的、分形自相似性的固有模态分量(IMF);在此基础上,基于有效信号和随机噪声的Hausdorff维数差异,识别混有随机噪声的IMF分量,对该分量进行相关的阈值滤波处理,从而实现有效信号和随机噪声的有效分离.文中从仿真信号试验出发,到模型地震数据和实际地震数据的测试处理,同时与传统的EMD处理结果相对比.结果表明,本文方法对地震随机噪声的衰减有更佳的压制效果.     

时频峰值滤波去噪技术及其应用

本文将时频峰值滤波(TFPF)去噪技术应用于共炮点地震资料的随机噪声压制.时频峰值滤波技术是通过频率调制将信号调制成解析信号的瞬时频率,利用解析信号的Wigner-Ville分布的峰值进行瞬时频率估计,恢复有效信号,与其它去噪方法相比,TFPF具有在较少的约束条件下压制强随机噪声的优点.本文针对实际地震资料的非线性特性,利用加窗的Wigner-Ville分布实现TFPF,使得地震信号在一个窗长内近似满足线性瞬时频率条件,减小由地震信号非线性引起的偏差.本文对共炮点地震记录做时频峰值滤波处理,滤波结果表明在地震勘探资料中存在强随机噪声的情况下,利用局部线性化处理的时频峰值滤波技术可以有效地压制地震资料中的随机噪声,恢复出湮没在随机噪声中的地震反射信号.信噪比提高3～6 dB.     

Dip‐adaptive singular‐value decomposition filtering for seismic reflection enhancement

We present a singular value decomposition (SVD) filtering method for the enhancement of coherent reflections and for attenuation of noise. The method is applied in two steps. First normal move‐out (NMO) correction is applied to shot or CMP records, with the purpose of flattening the reflections. We use a spatial SVD filter with a short sliding window to enhance coherent horizontal events. Then the data are sorted in common‐offset panels and the local dip is estimated for each panel. The next SVD filtering is performed on a small number of traces and a small number of time samples centred around the output sample position. Data in a local window are corrected for linear moveout corresponding to the dips before SVD. At the central time sample position, we sum over the dominant eigenimages of a few traces, corresponding to SVD dip filtering. We illustrate the method using land seismic data from the Tacutu basin, located in the north‐east of Brazil. The results show that the proposed method is effective and is able to reveal reflections masked by ground‐roll and other types of noise.     

Random noise attenuation using a structure-oriented weighted singular value decomposition

Singular value decomposition (SVD) is a useful method for random noise suppression in seismic data processing. A structure-oriented SVD (SOSVD) approach which incorporates structure prediction to the SVD filter is effcient in attenuating noise except distorting seismic events at faults and crossing points. A modified SOSVD approach using a weighted stack, called structure-oriented weighted SVD (SOWSVD), is proposed. In this approach, the SVD filter is used to attenuate noise for prediction traces of a primitive trace which are produced via the plane-wave prediction. A weighting function related to local similarity and distance between each prediction trace and the primitive trace is applied to the denoised prediction traces stacking. Both synthetic and field data examples suggest the SOWSVD performs better than the SOSVD in both suppressing random noise and preserving the information of the discontinuities for seismic data with crossing events and faults.

     

地电场观测中城市轨道交通干扰剔除

应用基于EMD的小波阈值去噪方法,去除地电场观测资料中轨道交通干扰,并将小波阈值去噪法和EMD去噪法的效果相比较,结果表明:该方法能够滤除地电场信号中的地铁干扰,同时保留原始信号中微小的突变,突出有用信息,提高地电场台站观测数据的使用率,有较好的去噪效果。基于EMD的小波阈值去噪方法可推广到其他地球物理观测资料的去噪分析,甚至地电场与地电阻率同场地观测中人工供电干扰信号的剔除。     

Low-rank seismic denoising with optimal rank selection for hankel matrices

Based on the fact that the Hankel matrix representing clean seismic data is low rank, low-rank approximation methods have been widely utilized for removing noise from seismic data. A common strategy for real seismic data is to perform the low-rank approximations for small local windows where the events can be approximately viewed as linear. This raises a fundamental question of selecting an optimal rank that best captures the number of events for each local window. Gavish and Donoho proposed a method to select the rank when the noise is independent and identically distributed. Gaussian matrix by analysing the statistical performance of the singular values of the Gaussian matrices. However, such statistical performance is not available for noisy Hankel matrices. In this paper, we adopt the same strategy and propose a rule that computes the number of singular values exceed the median singular value by a multiplicative factor. We suggest a multiplicative factor of 3 based on simulations which mimic the theories underlying Gavish and Donoho in the independent and identically distributed Gaussian setting. The proposed optimal rank selection rule can be incorporated into the classical low-rank approximation method and many other recently developed methods such as those by shrinking the singular values. The low-rank approximation methods with optimally selected rank rule can automatically suppress most of the noise while preserving the main features of the seismic data in each window. Experiments on both synthetic and field seismic data demonstrate the superior performance of the proposed rank selection rule for seismic data denoising.     

基于SW统计量的自适应时频峰值滤波压制地震勘探随机噪声研究

由于金属矿区地震记录中随机噪声性质复杂且信噪比低,常规降噪方法难以达到预期的滤波效果.时频峰值滤波(TFPF)方法是实现低信噪比地震勘探记录中随机噪声压制的有效方法,但其在复杂地震勘探随机噪声下时窗参数优化问题仍难以解决.本文充分利用地震勘探噪声的统计特性,结合Shapiro-Wilk(SW)统计量辨识地震勘探记录中的微弱有效信号,提出基于SW统计量的自适应时频峰值滤波降噪方法(S-TFPF).在S-TFPF方案中,对于有效信号集中区,S-TFPF方法根据信号频率特征,选择有利于信号保持的较短时窗长度;对于噪声集中区,按噪声方差自适应增加时窗长度,增强随机噪声压制能力.S-TFPF应用于合成记录和共炮点记录的滤波结果表明,与传统时频峰值滤波方法相比,S-TFPF方法可以有效抑制低信噪比地震勘探记录中的随机噪声,更好地恢复出同相轴.     

压制空间非平稳地震勘探随机噪声的ROAD径向时频峰值滤波算法

径向时频峰值滤波算法是一种有效保持低信噪比地震勘探记录中反射同相轴的随机噪声压制方法,但该算法对空间非平稳地震勘探随机噪声压制效果不理想.本文研究空间非平稳地震勘探随机噪声,即各道噪声功率不同的地震勘探随机噪声,其在径向滤波轨线上表征近似脉冲噪声,在径向时频峰值滤波过程中干扰相邻道滤波结果.为了减小空间非平稳随机噪声的影响,本文提出一种基于绝对级差统计量(ROAD)的径向时频峰值滤波随机噪声压制方法.该方法首先根据径向轨线上信号的绝对级差统计量检测空间非平稳地震勘探随机噪声,然后结合局部时频峰值滤波和径向时频峰值滤波压制地震勘探记录中的随机噪声.将ROAD径向时频峰值滤波方法应用于合成记录和实际共炮点地震记录,结果表明ROAD径向时频峰值滤波方法可以压制空间非平稳地震勘探随机噪声且不损害有效信号,有效抑制随机噪声空间非平稳对滤波结果的影响.与径向时频峰值滤波相比,ROAD径向时频峰值滤波方法更适用于空间非平稳地震勘探随机噪声压制.     

Singular value decomposition as a denoising tool for airborne time domain electromagnetic data

Airborne time domain electromagnetic (TDEM) surveys are increasingly carried out in anthropized areas as part of environmental studies. In such areas, noise arises mainly from either natural sources, such as spherics, or cultural sources, such as couplings with man-made installations. This results in various distortions on the measured decays, which make the EM noise spectrum complex and may lead to erroneous inversion and subsequent misinterpretations. Thresholding and stacking standard techniques, commonly used to filter TDEM data, are less efficient in such environment, requiring a time-consuming and subjective manual editing. The aim of this study was therefore to propose an alternative fast and efficient user-assisted filtering approach. This was achieved using the singular value decomposition (SVD). The SVD method uses the principal component analysis to extract into components the dominant shapes from a series of raw input curves. EM decays can then be reconstructed with particular components only. To do so, we had to adapt and implement the SVD, firstly, to separate clearly and so identify easily the components containing the geological signal, and then to denoise properly TDEM data.The reconstructed decays were used to detect noisy gates on their corresponding measured decays. This denoising step allowed rejecting efficiently mainly spikes and oscillations. Then, we focused on couplings with man-made installations, which may result in artifacts on the inverted models. An analysis of the map of weights of the selected “noisy components” highlighted high correlations with man-made installations localized by the flight video. We had therefore a tool to cull most likely decays biased by capacitive coupling noises. Finally, rejection of decays affected by galvanic coupling noises was also possible locating them through the analysis of specific SVD components. This SVD procedure was applied on airborne TDEM data surveyed by SkyTEM Aps. over an anthropized area, on behalf of the French geological survey (BRGM), near Courtenay in Région Centre, France. The established denoising procedure provides accurate denoising tools and makes, at least, the manual cleaning less time consuming and less subjective.     

奇异值分解(SVD)实现地震波场分离与去噪新思路

依据不同性质的地震信号（反射波、折射波、直达波、面波、VSP上\下行波、多次波、随机干扰等）之间在运动学、视速度和相干性上的差异,借助某种数学变换、SVD分解与重构联合、时域与频域结合的方式,通过这些间接的处理手段,把要提取的目标信号或要剔除的干扰信号转换到一种相干性更好的空间域中,再进行SVD分解与重构,最充分利用SVD滤波技术特点,实现地震波场分离与去噪,而不是直接对信号进行SVD分解与重构来实现地震波场分离与去噪,这样做可有效地避免以往对SVD波场分离与去噪技术应用空间狭窄、有效信号损失严重等缺陷性.     

Applications of Savitzky-Golay Filter for Seismic Random Noise Reduction

This article utilizes Savitzky–Golay (SG) filter to eliminate seismic random noise. This is a novel method for seismic random noise reduction in which SG filter adopts piecewise weighted polynomial via leastsquares estimation. Therefore, effective smoothing is achieved in extracting the original signal from noise environment while retaining the shape of the signal as close as possible to the original one. Although there are lots of classical methods such as Wiener filtering and wavelet denoising applied to eliminate seismic random noise, the SG filter outperforms them in approximating the true signal. SG filter will obtain a good tradeoff in waveform smoothing and valid signal preservation under suitable conditions. These are the appropriate window size and the polynomial degree. Through examples from synthetic seismic signals and field seismic data, we demonstrate the good performance of SG filter by comparing it with the Wiener filtering and wavelet denoising methods.