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.     

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.     

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.     

基于二维希尔伯特变换的地震倾角求取方法及其在构造导向滤波中的应用（英文）

在地震勘探数据采集中,随机噪声严重影响地震资料质量,给后期解释工作带来很大困难。如何在不损失剖面有效信息的前提下压制随机噪声,有效地提高地震资料的信噪比和保真度,是本文的研究目标。构造导向滤波技术的核心是构造方向表征的求取以及如何实现非平稳滤波,来达到提高地震数据信噪比和保真度的目的。本文首先通过分析函数二维导数与希尔伯特变换的频率响应关系,推导出了基于二维希尔伯特变换的非迭代地震同相轴倾角求取算子,进而达到了构造方向表征的求取;其次选取多项式拟合作为构造导向滤波中的非平稳滤波方法,扩展了非平稳多项式拟合的应用范围;最后沿构造倾角方向进行变振幅同相轴的非平稳多项式拟合,实现和构建了新的自适应构造导向滤波方法。理论模型和实际地震资料处理的结果表明,所提出的方法实现了既保护构造信息又有效地压制了随机噪声的目的。     

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。     

三维各向异性扩散滤波在地震数据处理中的应用

地震数据伴随的随机噪声,降低了地震资料品质,制约着后续的应用.在压制随机噪声的同时有效保护地质体边缘信息是地震资料去噪处理中的难点.本文利用三维地震数据空间的构造信息,构建结构张量和扩散张量,控制不同方向的平滑程度,利用二阶导数在地质体边缘出现极大值的特性,辅助判断地震同相轴的终断位置,借助连续性因子强化层理方向的扩散平滑,抑制地质体边缘、断点及裂缝位置的平滑.在理论算法研究的基础上,本文实现了地震数据的三维各向异性扩散滤波技术,应用该技术对叠前CMP道集、地震属性沿层切片、三维叠后地震数据等进行了去噪处理.实际应用结果表明,滤波后的地震资料信噪比明显提高,非终断地震同相轴连续性增强,地质体边缘特征变得更明显,断层的断点更清楚,为后续的地震解释和储层反演提供了可靠资料.     

用时频峰值滤波方法消减地震勘探资料中随机噪声的初步研究

时频峰值滤波算法是一种新颖的基于时频分析的信号增强算法,能够有效地消除随机噪声,恢复有效波信息.本文将这种时频分析算法用于消除地震勘探资料中的随机噪声,对淹没于随机噪声下的40道共炮点记录进行时频峰值滤波,恢复出来的共炮点记录可以清楚地表现原始记录同相轴的位置.经过对40道中任选两道（即第21道和第7道）滤波前后的子波形态Wigner\|Ville分布、傅立叶振幅谱等的比较,可知仅在谷值和峰值点误差较大,子波带宽相对误差小于25％.仿真试验表明信噪比可达－7dB,说明该方法可以有效地消减地震资料中的随机噪声.     

分形守恒定律在地震数据去噪与增强上的应用

分形守恒定律是一种基于偏微分方程的滤波方法.方程最显著的特征是将互为矛盾的两项——分形反扩散项与经典的扩散项结合在一起.反扩散项起信号增强的作用,而去噪的作用在扩散项中体现.通过分形指数,扩散与反扩散的系数可实现滤波器的调节.本文应用这个新颖的滤波模型来实现地震资料中随机噪声的消减,同时又能增强有效的地震信号.方程的求解基于傅里叶变换.对合成地震记录的测试表明。本算法在不同强度的噪声环境中(信噪比为-5 dB至10 dB)都能很好地恢复同相轴,提高信噪比.通过对实际共炮点资料的处理结果表明,基于分形守恒定律的新滤波方法能有效的压制随机噪声并改善同相轴的连续性.     

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.     

丘陵地带地震资料随机噪声压制新技术:高阶加权阈值函数的Shearlet变换

随着山地和丘陵地震勘探环境的复杂化,传统的消噪方法已经难以有效地压制地震记录中的随机噪声.Shearlet变换是一种新的多尺度多方向的时频分析方法,具有良好的稀疏表示特性,并且在每个尺度进行方向分解,非常适合用于地震信号随机噪声的压制.但是传统的Shearlet变换去噪方法采用的是硬阈值,在抑制随机噪声的同时也消除了很多有效信号,使得去噪之后的地震资料出现虚假的同相轴,为了解决这一问题我们提出高阶加权阈值函数.高阶加权阈值函数不但整体上连续性较好,而且克服了硬阈值函数存在剧烈的变化的缺点以及软阈值在处理较大Shearlet系数总存在恒定偏差的问题,同时保留了传统的软硬阈值函数的优点.实验结果表明这种基于高阶加权阈值函数的Shearlet变换去噪的方法,可以有效的消除模拟地震信号和实际丘陵地带地震信号中的随机噪声,同时很好的保留有效信号的幅度.     

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.     

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.

     

A SEMBLANCE-GUIDED MEDIAN FILTER1

Reiter , E.C., Toksoz , M.N. and Purdy , G.M. 1992. A semblance-guided median filter. Geophysical Prospecting 41 , 15–41. A slowness selective median filter based on information from a local set of traces is described and implemented. The filter is constructed in two steps, the first being an estimation of a preferred slowness and the second, the selection of a median or trimmed mean value to replace the original data point. A symmetric window of traces defining the filter aperture is selected about each trace to be filtered and the filter applied repeatedly to each time point. The preferred slowness is determined by scanning a range of linear moveouts within the user-specified slowness passband. Semblance is computed for each trial slowness and the preferred slowness selected from the peak semblance value. Data points collected along this preferred slowness are then sorted from lowest to highest and in the case of a pure median filter, the middle point(s) selected to replace the original data point. The output of the filter is therefore quite insensitive to large amplitude noise bursts, retaining the well-known beneficial properties of a traditional 1D median filter. Energy which is either incoherent over the filter aperture or lies outside the slowness passband, may be additionally suppressed by weighting the filter output by the measured peak semblance. This approach may be used as a velocity filter to estimate coherent signal within a specified slowness passband and reject coherent energy outside this range. For applications of this type, other velocity estimators may be used in place of our semblance measure to provide improved velocity estimation and better filter performance. The filter aperture may also be extended to provide increased velocity estimation, but will result in additional lateral smearing of signal. We show that, in addition to a velocity filter, our approach may be used to improve signal-to-noise ratios in noisy data. The median filter tends to suppress the amplitude of random background noise and semblance weighting may be used to reduce the amplitude of background noise further while enhancing coherent signal. We apply our method to vertical seismic profile data to separate upgoing and downgoing wavefields, and also to large-offset ocean bottom hydrophone data to enhance weak refracted and post-critically reflected energy.     

基于提升算法和百分位数软阈值的小波去噪技术

在地震勘探领域,随机噪声一直是影响地震信号信噪比的主要因素之一,如何从被干扰的地震信号中有效去除随机噪声并保护有用信号具有重要的意义.针对经典小波变换在计算效率方面的缺陷,本文推荐应用提升算法实现第二代小波变换的构建,分析和对比了提升算法(Lifting Scheme)下不同小波变换方法的特性,选取更加符合小波域去噪原理的CDF 9/7双正交小波变换作为基本算法,同时应用了简单、有效的百分位数(Percentiles)软阈值进行信噪分离.通过理论模型处理,本方法可以在去噪能力和保护有用信号之间找到很好的平衡点.实际剖面的处理效果表明,此方法不仅能有效的滤除随机噪声,而且很好地保护有用信号,提高地震数据分析的精确性.     

Automatic stacking‐velocity estimation using similarity‐weighted clustering

Local seismic event slopes contain subsurface velocity information and can be used to estimate seismic stacking velocity. In this paper, we propose a novel approach to estimate the stacking velocity automatically from seismic reflection data using similarity‐weighted k‐means clustering, in which the weights are local similarity between each trace in common midpoint gather and a reference trace. Local similarity reflects the local signal‐to‐noise ratio in common midpoint gather. We select the data points with high signal‐to‐noise ratio to be used in the velocity estimation with large weights in mapped traveltime and velocity domain by similarity‐weighted k‐means clustering with thresholding. By using weighted k‐means clustering, we make clustering centroids closer to those data points with large weights, which are more reliable and have higher signal‐to‐noise ratio. The interpolation is used to obtain the whole velocity volume after we have got velocity points calculated by weighted k‐means clustering. Using the proposed method, one obtains a more accurate estimate of the stacking velocity because the similarity‐based weighting in clustering takes into account the signal‐to‐noise ratio and reliability of different data points in mapped traveltime and velocity domain. In order to demonstrate that, we apply the proposed method to synthetic and field data examples, and the resulting images are of higher quality when compared with the ones obtained using existing methods.     

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.     

Erratic noise suppression using iterative structure‐oriented space‐varying median filtering with sparsity constraint

Erratic noise often has high amplitudes and a non‐Gaussian distribution. Least‐squares–based approaches therefore are not optimal. This can be handled better with non–least‐squares approaches, for example based on Huber norm which is computationally expensive. An alternative method has been published which involves transforming the data with erratic noise to pseudodata that have Gaussian distributed noise. It can then be attenuated using traditional least‐squares approaches. This alternative method has previously been used in combination with a curvelet transform in an iterative scheme. In this paper, we introduce a median‐filtering step in this iterative scheme. The median filter is applied following the slope direction of the seismic data to maximally preserve the energy of useful signals. The new method can suppress stronger erratic noise compared with the previous iterative method, and can better deal with random noise compared with the single‐step implementation of the median filter. We apply the proposed robust denoising algorithm to a synthetic dataset and two field data examples and demonstrate its advantages over three different noise attenuation algorithms.     

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.