三维地震资料叠前随机噪音压制

为提高叠前道集数据的信噪比,将频率域基于双向预测误差滤波的三维叠后随机噪音衰减方法(3DRNA),经过地震资料的道头修改和重置后,应用于叠前地震资料,实现三维叠前地震资料随机噪音的压制处理.某工区CMP道集和CRP道集随机噪音压制的试处理结果表明:该方法能够提高三维地震数据信噪比,得到的地震道集同相轴更清晰,可在不同的处理流程和步骤中串行应用,并且几乎可适用于所有三维地震资料.为提高速度分析精度、改善叠加效果、保持地震数据的AVO振幅特性和提高成果数据体的成像精度提供指导.     

十字交叉排列面波压制方法及应用

考虑面波特征和古龙断陷区地震资料面波强的特点,提出面波压制的十字交叉排列法.该方法将地震资料中的炮点线和检波点线重排后,进行三维Fourier变换;在频率—波数域滤波后,进行Fourier反变换,实现面波压制处理.古龙断陷区地震资料面波压制结果表明,十字交叉排列面波压制方法具有很好的保幅性和实用性,能够为地震资料保幅处理提供较好的叠前预处理方法,可以在深层地震资料处理中推广应用.     

λ-f域抛物Radon变换多次波压制方法

空间截断效应降低传统Radon变换压制多次波的精度.提出一种λ-f域抛物Radon变换方法,与传统最小二乘抛物Radon变换相比,地震数据变换到λ-f域后表现为直线的映射,更便于设计滤波函数,从而有效消除传统多次波压制过程中空间截断效应的影响.另外,新变量λ的引入,使Radon正反算子成为与频率无关的矩阵,可大幅度提高计算效率.理论模型和实际海洋地震数据多次波压制试算结果表明,提出的λ-f域抛物Radon变换法可有效实现多次波压制,提高地震数据的信噪比,具有较强的实用性.     

基于波动理论和抛物Radon变换的表面多次波压制技术

基于波动方程预测相减的表面多次波压制方法,能较好压制地下复杂介质产生的多次波,但是计算成本较高。基于滤波方法压制多次波计算效率较高,对一次波和多次波存在较大时差的地震数据有明显的压制效果,但是对来自复杂介质的地震数据较难得到良好的压制效果。将波动方程法和滤波法结合,提出一种基于抛物Radon变换和波动方程的多次波压制方法。首先应用表面多次波形成机理预测多次波,利用对应的预测多次波的地震记录和初始地震记录,在抛物Radon域设计自适应滤波函数,通过准确滤波得到多次波在抛物Radon域的能量;然后通过反变换得到多次波,从原始地震记录中减去多次波,得到压制多次波后的结果。水平层状模型及SMAART模型测试结果表明,该方法结合两种方法的优点,能够准确、高效压制来自地下复杂介质产生的表面多次波。     

基于频率拓展的表面多次波压制方法

为消除多次波预测中多余的子波效应,拓宽地震数据的频带,使预测多次波的走时、振幅和相位与实际的多次波相吻合,根据原始数据,衍生频率拓展道等多道地震数据;根据最小二乘原则,得到自适应滤波器和压制多次波后的有效波数据。结果表明:基于频率拓展的多次波压制方法,能够有效压制多次波,提高地震资料的信噪比;对水平层状模型测试结果频谱分析,自适应相减后得到的有效波同预测的多次波相比,频带拓宽,高频能量增强;通过对SMAART模型测试,基于频率拓展的多次波压制方法在噪声较强的情况下,也能得到很好的多次波压制效果。对于地质结构较复杂的情况,与抛物Radon变换滤波法相比,该方法多次波压制效果更好,并且不损害有效波能量。     

沂沭断裂带深反射地震资料处理

地震时间剖面可以对地质目标进行精细刻画,但由于变质岩、侵入岩地区存在地质体不规则、地质界线间波阻抗差异不明显、断裂构造倾角高陡等特征,导致地震原始资料具有波场复杂、干扰波发育、资料信噪比低等特点。为获得客观反应实际地质情况的沂沭断裂带深反射地震剖面,通过对研究区原始地震资料细致分析,针对各种干扰波的不同特性特征,分别采取针对性压制措施,进行组合去噪,在压制干扰的同时最大限度保护了有效信号。本次地震资料处理以高保真和高信噪比为目标,通过边处理边解释不断优化处理流程和处理参数。在处理过程中对静校正、叠前去噪、速度场建立以及波场空间归位等关键工作进行了重点研究,最终获得了高品质地震叠加和偏移时间剖面。反射剖面首次揭示了沂沭断裂带及其两侧岩石圈精细结构,为研究沂沭断裂带深部结构及对资源、环境的影响提供了可靠的地震学依据。     

广义S变换在地震数据处理中的应用与展望

总结了目前常见的广义S变换类型、原理与特点,归纳了现今广义S变换在时频域滤波去噪、时频谱分解、初至波识别与拾取及面波干扰压制等地震资料处理方面的研究成果。最后,展望了广义S变换在地震弱信号识别与提取、地震资料精细时变滤波、面波频散特性分析、背景噪声成像等研究方面的应用潜能。     

地震勘探中多次反射折射波的影响分析及应对措施——以滕县煤田某煤矿地震勘探为例

多次反射折射波影响地下地质目标体的地震成像。在鲁西南某矿地震勘探区内,由于第四系与侏罗系底砾岩直接接触,且第四系厚度薄,反射系数大,产生了多次反射折射波。该研究分析了多次反射折射波产生的机理,优化调整了勘探设计,采取规则噪音滤波、叠前多道预测反褶积、精确的速度等针对性的处理措施,有效消除了多次反射折射波对有效波成像的影响,提高了资料信噪比,取得了较好的效果。     

海上拖缆三维地震数据规则化技术研究

电缆的羽状漂移导致海上三维拖缆地震资料采集出现偏移距、方位角及覆盖次数的不规则现象,常规面元均化技术难以实现CMP道集内数据的规则性。在分析海上地震数据不规则性特点的基础上,将反漏频傅里叶变换(ALFT)数据规则化技术应用于LD地区WZ油田的CMP道集内数据的规则化处理,基于规则化海上地震数据的叠前偏移成像效果,评价该方法的有效性。结果表明,地震数据规则化技术可以有效地提高数据的信噪比,改善同相轴的连续性,明显提高成像质量。      

高精度广义KL变换波场分离与去噪

KL滤波是地震数据处理中的重要方法技术之一。针对在实际中如何有效地应用好KL变换的问题，结合有效波同相轴高精度拾取和排齐技术，采用奇异值分解（SVD）算法实现KL变换，使KL滤波适用于任意弯曲同相轴的分离与消除。理论模型计算和实际资料处理试验表明，使甬该方法能有效地分离相干噪声和随机噪声，提高地震记录的信噪比，简单实用．便于操作。     

Improvement of S/N ratio of seismic data by hyperbolic filter algorithm

This paper deals with the implementation of the hyperbolic filter algorithm for noise suppression of seismic data. Known the velocity of reflection event, utilizes the resemblance of reflection signal in each seismic trace, the hyperbolic filter algorithm is effective in enhance reflection event and suppress the random noise. This algorithm is used to CDP gathers also is compared with the algorithm of τ-p transform. Simulation shows the hyperbolic filter is effective and better than τ-p transform.     

基于CEEMDAN和小波变换的地震信号随机噪声压制新方法

提出一种结合自适应噪声完备集合经验模态分解（CEEMDAN）和小波变换（WT）的地震信号去噪新方法CEEMDAN-WT。首先利用CEEMDAN将地震信号自适应地分解为若干个固有模态函数（IMF）和余量;然后计算各分量与原始信号的皮尔森相关系数,对处在不同相关系数阈值区间内的分量分别作小波滤波、维持原状及直接剔除等处理,并进行线性重构;最后构建样本熵变化量、互信息、信噪比等指标体系,定量评估去噪效果。模拟实验与实测数据（青海玛多地震）计算结果表明,与EMD、EEMD等方法相比,CEEMDAN-WT方法能有效抑制随机噪声的影响,提高信噪比,并且地震信号的精细化重构效果较好,信号有效成分得到较大保留。     

Random seismic noise attenuation by learning-type overcomplete dictionary based on K-singular value decomposition algorithm

The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.     

Seislet transform denoising based on total variation minimization

Attenuation of noise is a persistent problem in seismic exploration. The authors use conventional denoising method to remove noise which may cause vibration near the discontinuity called pseudo-Gibbs artifact.In order to remove the artifact,the study proposed a method combining the seislet transform and total variation minimization. Firstly,the data are converted into the seislet transform domain. Secondly,the hard threshold was used for eliminating the noise and keep useful signal,which is the initial input for the next step. Finally,total variation minimization dealed with denoised data to recover boundary information and further eliminated the noise. Synthetic data examples show that the method has feasibility in eliminating random noise and protecting detailed signal,and also shows better results than the classic f-x deconvolution. The field data example also shows effective in practice. It can remove the noise and preserve the discontinuity signal at the same time.     

Study of denoising method for nonhyperbolic prestack seismic reflection data

Removing random noise in seismic data is a key step in seismic data processing. A failed denoising may introduce many artifacts, and lead to the failure of final processing results. Seislet transform is a wavelet-like transform that analyzes seismic data following variable slopes of seismic events. The local slope is the key of seismic data. An earlier work used traditional normal moveout(NMO) equation to construct velocity-dependent(VD) seislet transform, which only adapt to hyperbolic condition. In this work, we use shifted hyperbola NMO equation to obtain more accurate slopes in nonhyperbolic situation. Self-adaptive threshold method was used to remove random noise while preserving useful signal. The synthetic and field data tests demonstrate that this method is more suitable for noise attenuation.     

基于小波特征和神经网络的天然地震与人工爆破自动识别

采用福建地区天然地震和人工爆破事件波形记录，通过一维离散小波变换（DWT）及4层小波包变换（WPT）对信号进行分解，提取出用于识别的4种波形小波特征：小波能量比特征、小波包能量比特征、小波包香农熵特征及小波包对数能量熵，此外还提取出P/S震相振幅比；采用BP神经网络对4种小波特征及分别加入P/S震相振幅比的组合特征进行识别效果检验，结果表明，单小波判据小波能量比特征识别效果好；双判据组合P/S震相振幅比和小波包对数能量熵的组合识别效果最好，可考虑作为实际天然地震与人工爆破在线自动识别系统的识别判据。     

Stationary phase point extraction based on high-resolution radon transform and its application to multiple attenuation by wavefield extrapolation

Multiple prediction and subtraction techniques based on wavefield extrapolation are effective for suppressing multiple related to water layers.In the conventional wavefield extrapolation method, the multiples of the seismic data are predicted from the known total wave field by the Green function convoluted with each point of the bottom.However, only the energy near the stationary phase point has an effect on the summation result when the convolutional gathers are added.The research proposed a stationary phase point extraction method based on high-resolution radon transform.In the radon domain, the energy near the stationary phase point is directly added along the convolutional gathers curve, which is a valid solution to the problem of the unstable phase of the events of multiple.The Curvelet matching subtraction technique is used to remove the multiple, which improved the accuracy of the multiple predicted by the wavefield extrapolation and the artifacts appearing around the events of multiple are well eliminated.The validity and feasibility of the proposed method are verified by the theoretical and practical data example.