局部相关加权中值滤波技术及其在叠后随机噪声衰减中的应用

随机噪声的衰减和同相轴连续性的提高可以极大地改善地震资料解释的精度.本文提出一种新的滤波技术,既能够有效地衰减随机噪声又可以很好地保护地震资料中的断层等信息不被破坏,增强地震剖面中弯曲、倾斜同相轴的连续性.该方法结合新的加权中值滤波技术和两种构造信息保护滤波策略,实现基于预测数据体和基于倾角走向的加权中值滤波.通过设计...     

基于结构中值滤波的CRP道集优化处理技术

共反射点道集中的噪声与剩余时差影响着偏移剖面的质量和叠前反演的精度.本文采用二次多项式拟合地震数据对地层倾向进行估计,并基于结构中值滤波方法对共反射点道集进行优化处理.在计算时窗内,对道集中每一个样点的二次多项式拟合系数进行精确扫描以确定样点所在同相轴方向并在同相轴方向进行中值滤波即结构中值滤波,滤波后道集信噪比得到提高同时保护了地层边缘特征.由于相关性在叠加剖面中要优于存在剩余时差的道集间,因此本文在叠加剖面上通过建立模型道来进行剩余时差校正即通过计算滑动时窗内待校正地震道和模型地震道间的互相关值,以最优化原则逐道估计剩余校正量,得到每个共反射点道集的剩余校正量模型,然后对剩余校正量模型在空间上做平滑处理,最后对共反射点道集进行时差校正.理论模型数据和实际数据的处理结果表明,该方法能够很好地解决道集中的信噪比和剩余时差问题,提高了共反射点道集和叠加剖面的整体质量.     

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.     

基于非平稳相似性系数的构造导向滤波及断层检测方法

不连续地质体（如断层）的自动检测一直以来都是叠后地震数据解释中的关键问题之一,尤其在三维情况中尤为重要.然而,大多数边缘检测和相干算法都对随机噪声很敏感,随机噪声衰减是叠后地震数据解释的另一个主要问题.针对构造保护去噪和断层检测问题,本文基于非平稳相似性系数完善一种构造导向滤波方法并且提出一种自动断层检测方法,形成了一套匹配的处理技术.该构造导向滤波既能够有效地衰减随机噪声又可以很好地保护地震资料中的断层等信息不被破坏,增强地震剖面中弯曲、倾斜同相轴的连续性.根据地震数据局部倾角走向,利用相邻道构建当前地震道的预测,通过预测道的叠加得到参考道,计算预测道与参考道之间的非平稳相似性系数可以设计出数据驱动的加权中值滤波.另一方面,预测道与原始道之间的非平稳相似性系数能够用于带有断层指示性的相干分析.这两种方法都基于构造预测和非平稳相似性系数,但是使用不同的调节参数和处理方案.理论模型和实际数据的处理结果证明了本文提出构造导向滤波和断层检测方法的有效性.     

非线性各向异性扩散滤波器用于三维地震资料噪声衰减与结构特征增强

消除地震资料中的干扰噪声,增强地质沉积特征,对地震资料的后继处理和解释都有重要的意义.本文重点论述一种基于偏微分方程的非线性各向异性扩散滤波技术.采用梯度结构张量估计局部地层方向,度量地质特征的规则程度,以此约束扩散滤波器沿着地层方向作扩散滤波,并且通过控制在断层和河道等不连续性区域的滤波程度,使得扩散滤波器具有优异的保持边缘特性.对大庆油田的一块实际地震资料的处理结果表明,该方法可有效消除非相干噪声,增强地震反射同相轴的连续性,突出地质沉积特征,提高资料的信噪比,整体改善地震资料的品质.     

基于f-x域流式预测滤波器的地震随机噪声衰减方法

随机噪声的影响在地震勘探中是不可避免的,常规的随机噪声压制方法在处理中往往会破坏具有时空变化特征的非平稳有效地震信号,影响地震数据的准确成像.当前油气勘探的目标已经转变为“两宽一高”,随着数据量的增大,对去噪方法的处理效率也提出了更高的要求.因此,开发高效的非平稳地震数据随机噪声压制方法具有重要意义.预测滤波技术广泛用于地震随机噪声的衰减,本文基于流式处理框架提出一种新的f-x域流式预测滤波方法,通过在频率域建立预测自回归方程,运用直接复数矩阵逆运算代替迭代算法求解非平稳滤波器系数,实现时空变地震同相轴预测,提高自适应预测滤波的计算效率.通过与工业标准的FXDECON方法和f-x域正则化非平稳自回归(RNA)方法进行对比,理论模型和实际数据的测试结果表明,提出的f-x域流式预测滤波方法能更好地平衡时空变有效信号保护、随机噪声压制和高效计算三者之间的关系,获得合理的处理效果.     

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.     

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.     

模糊嵌套多级中值滤波方法及其在地震数据处理中的应用

多级中值滤波器在地震数据处理应用中,其滤波长度越大,消噪效果越好,但同时也会破坏有效信息. 如果为了保护有效信息而减少滤波长度,又会造成大量的噪声不能消除. 本文提出一种新的模糊嵌套多级中值滤波器,设计一个阀值作为判断参数,使滤波器能够在消除随机噪声时采用长滤波器滤波,而在保留有效信息时采用短滤波器滤波,从而既能很好地消除随机噪声,又能最大限度地保护有效信息,保留有效信息的细节结构. 经过模型分析和实际资料处理都取得了很好的效果.     

一种基于自适应分子分解的地层吸收补偿方法

本文提出了一种基于自适应分子分解的地层吸收补偿方法。该方法能有效克服时频域地层吸收补偿方法中局部分析时窗端点反射子波截断和窗内子波重叠干涉对补偿效果带来的影响。该方法具体实现如下:首先根据地震信号振幅谱的时变特性,构造一组自适应分子标架,将地震信号变换到时频域;然后在时频域提取每个时窗内地震信号振幅谱的慢变分量(时变子波振幅谱);再在每个时窗内,利用相关系数构造目标函数,反演吸收补偿滤波器参数,得到吸收补偿滤波器;接着用吸收补偿滤波器对各时窗内信号的时频谱进行补偿,最后重构补偿后的时频系数,得到补偿后的地震记录。自适应分子分解的引入,使得本文方法可用于含有薄层(或薄互层)的非均匀粘弹性介质反射地震记录的地层吸收补偿。文中给出了模型及实际资料算例,验证了本文提出的方法的有效性。     

自适应非局部均值地震随机噪声压制(英文)

非局部均值滤波是一种基于图像信息冗余的去噪方法,其认为图像自身的有效结构具有一定的重复性,而随机噪声则不具备这一特点,通过利用图像本身的自相似性来达到压制随机噪声的目的,是一种全局的去噪方法。本文把这一思想引入地震数据随机噪声压制中,针对传统非局部均值滤波计算量过大的问题,文章采用分块非局部均值的方式来减少计算量;针对滤波参数选取会影响非局部均值滤波效果的问题,提出一种简单的自适应滤波参数地震数据分块非局部均值算法。模型和实际数据处理结果表明:相对于传统的去噪算法(如f-x反褶积),该方法在压制随机噪声的同时对有效信号保护地更好,具有更高的保真度,更有利于后续的处理和解释工作。     

基于核函数主分量的维纳滤波方法研究

针对强随机噪声地震资料背景下经典维纳滤波方法在信号的保幅及高维数据空间求解过程中产生病态矩阵的问题,提出利用核函数主分量维纳滤波压制强地震勘探随机噪声.首先利用线性核函数将地震信号映射到特征空间,再通过主分量分析方法提取地震数据主分量进行数据降维,并得到核主分量维纳滤波因子,从而进行核主分量维纳滤波（K-WPC）.正演仿真及对实际地震资料处理表明,该方法对随机噪声有较好的压制作用,保幅效果也令人满意.     

基于混合时频分析技术的地震数据噪声压制(英文)

针对复杂地质结构、陡倾角相干噪声、空间采样不均匀等情况下F-x域反褶积去噪技术的不足,提出首先应用具有时-频聚集性度量准则的广义S变换将时间-空间域的地震数据变换至时间-频率-空间域(t-f-x)的数据,在t-f-x域中对每一个频率切片应用经验模态分解(EMD),移除噪声占主导地位的本征模态函数以压制相干和随机噪声的滤波方法。模型分析表明第一本征模态函数表征的高频信息以噪声为主,移除第一本证模态函数可以达到压制噪声的目的。经广义S变换后形成t-f-x域中EMD滤波方法等效于具有依赖于空间位置、频率、高波数截断特征的自适应f-k滤波。此滤波方法考虑了数据的局部时-频特征,且具有执行简单的特点。与AR预测滤波方法比较,此法滤除的成分包含较少的低波数的信息,滤除的成分非常的局部化,且获得结果没有表现出过度平滑的特征。实际资料的应用表明在经广义S变换后形成t-f-x域中运用EMD滤波方法能够有效地压制随机和陡倾角相干噪声。     

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.     

Structural complexity-guided predictive filtering

Random noise attenuation utilizing predictive filtering achieves great performance in denoising seismic data. Conventional predictive filtering methods are based on fixed filter operators and neglect the complexity of structures. In this way, the denoised data cannot meet the requirement of balancing the signal preservation and noise removal. In this study, we proposed a structural complexity-guided predictive filtering method that utilizes an adapted filter operator to adjust the changes of structural complexity. The proposed structural complexity-guided predictive filtering mainly consists of two stages. A slope field information is acquired according to plane-wave destruction to assess the structural complexity. In addition, an adaptive filter operator is obtained to denoise the seismic data according to the adaptive factor. Both synthetic data and real seismic profiles are employed to examine the denoising capacity and flexibility of the refined predictive filtering using adaptive lengths. The analysis of the predicted results shows that adaptive predictive filtering is powerful and has the ability to eliminate random noises with negligible distortions.     

Nonlinear structure‐enhancing filtering using plane‐wave prediction*

Attenuation of random noise and enhancement of structural continuity can significantly improve the quality of seismic interpretation. We present a new technique, which aims at reducing random noise while protecting structural information. The technique is based on combining structure prediction with either similarity‐mean filtering or lower‐upper‐middle filtering. We use structure prediction to form a structural prediction of seismic traces from neighbouring traces. We apply a non‐linear similarity‐mean filter or an lower‐upper‐middle filter to select best samples from different predictions. In comparison with other common filters, such as mean or median, the additional parameters of the non‐linear filters allow us to better control the balance between eliminating random noise and protecting structural information. Numerical tests using synthetic and field data show the effectiveness of the proposed structure‐enhancing filters.     

Incorporation of a Non-linear Image Filtering Technique for Noise Reduction in Seismic Data

Seismic noise is a fundamental part of seismic data which cannot be avoided when conducting any seismic survey. It consists of coherent and random noise. Noise removal or filtering is one of the major concerns in the field of seismic processing. In this paper, we introduce an image filtering technique based on a detection-estimation algorithm for Gaussian and random noise removal in seismic data, namely the trilateral filter, based on a statistic called rank-ordered absolute differences. The non-linear and adaptive behaviour of this filter makes it very robust in the presence of random and coherent noise, in addition to its computational simplicity and its ability to automatically identify noise in data. We have modified the strategy of trilateral filtering by adapting the rank-ordered absolute differences formula in order to extract the signal component. We have successfully used this filter for the removal of surface waves and random spiky noise from synthetic and field data. Results are very encouraging and show the superiority of this filter compared with other filters, particularly when used recursively.     

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.