提高地震数据信噪比的PCA方法

由于地震数据中存在各种各样的噪声,怎样去压制或者滤除这些噪声,以至提高信噪比成为当前地震信号处理中关键步骤.本文根据地震数据中随机噪声干扰和可能存在的工业单频噪声干扰的特点,采用主成分分析技术,即通过正交分解得到数据矩阵的主分量,并利用不同的主分量进行信号重构,从而达到去除单频干扰和随机噪声的目的,有效地提高了地震数据...     

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.     

保幅低频面波压制方法与应用

面波是陆地地震勘探中的一种强能量规则干扰,面波的存在对地震数据成像有很大影响,因此需要对面波予以压制.常规面波压制方法只考虑面波的强能量特性,很容易损伤面波频带内的有效信号,难以满足地震资料保幅处理的要求.在传统方法的基础上,本文提出低频弱信号保护的面波压制方法,该方法在有效地消除面波干扰的同时,最大程度的减少了低频有效信息的损失,提高地震资料的信噪比和保幅性,松辽盆地北部西部斜坡地区实际地震资料的处理表明,该方法是一种有效的相对保幅的面波压制方法,可为岩性油气藏勘探提供重要的基础数据,具有很强的实用性.     

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.     

一种优化预测滤波压制随机噪声的方法

针对低信噪比地震资料中的随机噪声干扰,提出了一种优化的预测滤波方法.通过对AR模型数学表达式进行改写,然后引入反褶积代价函数,最后推导得到压制随机噪声的递归算法.该算法实现了单道压制随机噪声,保护了有效信号的细节部分,比基于常规最小二乘代价函数的滤波算法收敛更快.通过模拟和实际地震资料验证表明,该方法能较好地压制低信噪比资料中的随机噪声干扰,同时能较好地保护有效地震信号.     

低信噪比地震资料FDOC-seislet变换阈值消噪方法研究

地震数据本质上是时变的,不仅有效同相轴表现出确定性信号的时变特征,而且复杂地表和构造条件以及深部探测环境总是引入时变的非平稳随机噪声.标准的频率-空间域预测滤波只适合压制平面波信号假设下的平稳随机噪声,而处理非平稳地震随机噪声时,需要将数据体分割为小窗口进行分析,但效果不够理想,而传统非预测类随机噪声压制方法往往适应性不高,因此开发能够保护地震信号时变特征的随机噪声压制方法具有重要的工业价值.压缩感知是近年出现的一个新的采样理论,通过开发信号的稀疏特性,已经在地震数据处理中的数据插值以及噪声压制中得到了应用.本文系统地分析了压缩感知理论框架下的地震随机噪声压制问题,建立了阈值消噪的数学反演目标函数;针对时变有效信息具有的可压缩性,利用有限差分算法求解炮检距连续方程,构建有限差分炮检距连续预测算子(FDOC),在seislet变换框架下,提出一种新的快速稀疏变换域———FDOC-seislet变换,实现地震数据的高度稀疏表征;结合非平稳随机噪声不可压缩的特征,提出了一种整形迭代消噪方法,该方法是一种广义的迭代收缩阈值(IST)算法,在无法计算稀疏变换伴随算子的条件下,仍然能够对强噪声环境中的时变有效信息进行有效恢复.通过对模型数据和实际数据的处理,验证了FDOC-seislet稀疏变换域随机噪声迭代压制方法能够在保护复杂构造地震波信息的前提下,有效地衰减原始数据中的强振幅随机噪声干扰.     

基于方向可控滤波的地震勘探随机噪声压制

针对地震勘探随机噪声的压制,本文应用拉伸厄米特高斯函数设计出方向可控滤波器.根据时空域上随机噪声的无方向性与有效信号的有向性的区别,通过局部数字特征,对数据进行选择后重组信号.方向选择性的增加,使得滤波过程能与不同方向的轴进行匹配,噪声被压制的同时保持信号的幅度;方向可调性,使得计算效率提高,且所需存储空间减少.仿真实验表明,采用此方法,信号保幅性和去噪效果均比传统的小波算法以及Curvelet变换好,在-5db信噪比下,本文方法保幅度为92.99%,信噪比提升221.774%,在实际地震信号处理中有明显的抑制噪声、保持有用信号的效果.     

基于数据驱动和反演策略的时空域随机噪声衰减方法（英文）

常规的时间-空间域和频率-空间域预测滤波方法假设地震记录由地震信号和随机噪声两部分构成,即所谓的加噪声模型,但是,在对随机噪声进行估算时,又假设随机噪声可以通过预测误差滤波器由地震记录中进行预测,即所谓的源噪声模型。这种前后不一致的噪声模型降低了该类方法的去噪能力和保幅性能。为此,本文提出了一种基于反演的时空域随机噪声衰减方法。它首先从地震数据中估算预测滤波算子,该算子表征了地震信号的可预测性,自适应地描述了地震信号的空间结构。在得到预测误差算子之后,将该算子作为正则化约束引入到地震信号反演系统,由含有随机噪声的地震数据直接反演地震信号。不同于常规随机噪声衰减方法,该方法将随机噪声衰减问题归结为正则化约束下的地震信号反演问题,克服了常规方法噪声模型的不一致性问题。我们采用模型数据和实际数据进行了实验分析,并与常规方法进行了效果对比。实验结果表明:与常规方法相比,本文方法在噪声压制的同时,没有对有效信号产生明显伤害,具有更好的振幅保持能力。     

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。     

基于Hessian矩阵的地震随机噪声压制方法

随机噪声的压制在提高地震资料信噪比方面发挥重要作用.考虑到传统去噪方法在构造复杂地区难以取得理想的去噪结果,本文提出基于Hessian矩阵特征值对应的线性目标关系在多个尺度上对随机噪声进行压制.该方法将地震信号看作不同尺度的曲线,从而利用Hessian矩阵在曲线检测方面表现出的良好性能实现信噪分离.该方法与传统方法相比不受地层倾角的限制,因此能够处理构造较为复杂地区的地震数据.利用模型及实际资料对该方法进行了验证并与传统方法F-X反褶积的去噪结果做对比,结果表明基于Hessian矩阵的随机噪声压制方法在构造复杂地区能够保持有效信号的完整性.     

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.     

Inversion-based data-driven time-space domain random noise attenuation method

Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter (PEF), is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data’s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.     

Ricker子波核支持向量回归的Mercer条件拓展问题研究

Ricker子波核支持向量回归方法是消减地震勘探记录强随机噪声的新滤波技术.用判定支持向量允许核函数的Mercer条件探讨Ricker子波核函数的有效性,经过数值计算相应的核矩阵的最小特征值,发现在一个较大范围内存在极小的负值带,数量级为10^-13~10^-16,且在正值带中亦存在10^-13~10^-15数量级的量.考虑到正负极小量值的计算误差机理相近,认为判定核函数有效性的Mercer条件在工程应用时有可能适当放宽,即核矩阵不严格半正定,接近半正定亦可.为了将Ricker子波核支持向量回归滤波方法向实际应用发展,本文对不同的理论模型的Ricker子波核滤波和小波变换滤波、自适应维纳滤波这三种技术的效果进行了比较,包括时域波形、频域振幅谱、滤波前后的信噪比以及均方误差等方面.结果表明,Ricker子波核滤波方法优于另两种方法.为实际应用Ricker子波核滤波方法奠定基础.     

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.     

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.     

基于结构自适应中值滤波器的随机噪声衰减方法

本文提出一种保护断层、裂缝等地层边缘特征的结构自适应中值滤波器,用于衰减地震资料中的随机噪声.基于地震反射同相轴局部呈线型结构的假设,采用梯度结构张量估计地层倾向,分析地层结构的规则程度,在此基础上引入地震剖面中线型和横向不连续性两种结构特征的置信度量.结构自适应中值滤波器根据这两种置信度量调整滤波器窗函数的尺度和形状,根据地层倾角调整滤波器窗函数的方向,从而使得滤波操作窗能够最佳匹配信号的局部结构特征.将本文方法用于合成和实际数据的处理,并与两种常用中值滤波方法进行对比,结果表明,该方法能够更好地解决地震剖面的随机噪声衰减和有效信号保真的问题,在增强反射同相轴的横向一致性的同时有效保持了剖面内的地层边缘和细节特征,显著改善了地震资料的品质.     

Seismic directional random noise suppression by radial‐trace time–frequency peak filtering using the Hurst exponent statistic

Radial‐trace time–frequency peak filtering filters a seismic record along the radial‐trace direction rather than the conventional channel direction. It takes the spatial correlation of the reflected events between adjacent channels into account. Thus, radial‐trace time–frequency peak filtering performs well in denoising and enhancing the continuity of reflected events. However, in the seismic record there is often random noise whose energy is concentrated in certain directions; the noise in these directions is correlative. We refer to this kind of random noise (that is distributed randomly in time but correlative in the space) as directional random noise. Under radial‐trace time–frequency peak filtering, the directional random noise will be treated as signal and enhanced when this noise has same direction as the signal. Therefore, we need to identify the directional random noise before the filtering. In this paper, we test the linearity of signal and directional random noise in time using the Hurst exponent. The time series of signals with high linearity lead to large Hurst exponent value; however, directional random noise is a random series in time without a fixed waveform and thus its linearity is low; therefore, we can differentiate the signal and directional random noise by the Hurst exponent values. The directional random noise can then be suppressed by using a long filtering window length during the radial‐trace time–frequency peak filtering. Synthetic and real data examples show that the proposed method can remove most directional random noise and can effectively recover the reflected events.     

Mixed Cadzow filtering method in fractional Fourier domain

Conventional frequency domain singular value decomposition (SVD) filtering method used in random noise attenuation processing causes bending event damage. To mitigate this problem, we present a mixed Cadzow filtering method based on fractional Fourier transform to suppress random noise in 3D seismic data. First, the seismic data is transformed to the time-frequency plane via the fractional Fourier transform. Second, based on the Eigenimage filtering method and Cadzow filtering method, the mixed high-dimensional Hankel matrix is built; then, SVD is performed. Finally, random noise is eliminated effectively by reducing the rank of the matrix. The theoretical model and real applications of the mixed filtering method in a region of Sichuan show that our method can not only suppress noise effectively but also preserve the frequency and phase of effective signals quite well and significantly improve the signal-to-noise ratio of 3D post-stack seismic data.     

Application of a cascading filter implemented using morphological filtering and time–frequency peak filtering for seismic signal enhancement

Inspired by the idea of the iterative time–frequency peak filtering, which applies time–frequency peak filtering several times to improve the ability of random noise reduction, this article proposes a new cascading filter implemented using mathematic morphological filtering and the time–frequency peak filtering, which we call here morphological time–frequency peak filtering for convenience. This new method will be used mainly for seismic signal enhancement and random noise reduction in which the advantages of the morphological algorithm in processing nonlinear signals and the time–frequency peak filtering in processing nonstationary signals are utilized. Structurally, the scheme of the proposed method adopts mathematic morphological operation to first preprocess the signal and then applies the time–frequency peak filtering method to ultimately extract the valid signal. Through experiments on synthetic seismic signals and field seismic data, this paper demonstrates that the morphological time–frequency peak filtering method is superior to the time–frequency peak filtering method and its iterative form in terms of valid signal enhancement and random noise reduction.