基于地震信号波形形态差异的面波噪声稀疏优化分离方法

实际地震信号通常可表示为具有波形特征差异的多种基本波形信号的线性组合,如叠前道集中的工频干扰噪声与有效波信号、面波噪声与体波信号等.选择单一数学变换方法,往往不易实现地震信号的稀疏表示.近年来发展的形态成分分析理论,通过联合多种数学变换,可实现对复杂信号的稀疏表示.本文根据单道地震记录中面波与体波信号波形结构特征的差异性,提出一种基于形态成分分析的面波噪声衰减方法.针对面波的低频、窄带以及频散特性选择一维平稳小波变换作为其稀疏表示字典,而针对体波波形的局部相关特性选择局部离散余弦变换作为其稀疏表示字典,建立基于双波形字典的形态成分分析模型,通过求解该稀疏优化问题获得最终的信噪分离结果.理论模型和实际地震资料处理证实该方法不仅能够衰减单炮地震记录中的强面波干扰噪声,同时能够更好地保护有效信号的波形特征与频谱带宽,为地震资料的后续处理和分析提供良好的数据基础.     

Ground roll wave suppression based on wavelet frequency division and radial trace transform

Ground roll waves interfere with seismic data. The suppression of ground roll waves based on the division of wavelet frequencies considers the low-frequency characteristics of ground roll waves. However, this method will not be effective when the ground roll wave and the effective signal have the same frequency bands because of overlapping. The radial trace transform (RTT) considers the apparent velocity difference between the effective signal and the ground roll wave to suppress the latter, but affects the low-frequency components of the former. This study proposes a ground roll wave suppression method by combining the wavelet frequency division and the RTT based on the difference between the ground roll wave velocity and the effective signal and their energy difference in the wavelet domain, thus making full use of the advantages of both methods. First, we decompose the seismic data into different frequency bands through wavelet transform. Second, the RTT and low-cut filtering are applied to the low-frequency band, where the ground roll waves are appearing. Third, we reconstruct the seismic record without ground roll waves by using the inverse RTT and the remaining frequency bands. The proposed method not only improves the ground roll wave suppression, but also protects the signal integrity. The numerical simulation and real seismic data processing results suggest that the proposed method has a strong ability to denoise while preserving the amplitude.     

Ground roll attenuation using a time-frequency dependent polarization filter based on the S transform

The ground roll and body wave usually show significant differences in arrival time, frequency content, and polarization characteristics, and conventional polarization filters that operate in either the time or frequency domain cannot consider all these elements. Therefore, we have developed a time-frequency dependent polarization filter based on the S transform to attenuate the ground roll in seismic records. Our approach adopts the complex coefficients of the S transform of the multi-component seismic data to estimate the local polarization attributes and utilizes the estimated attributes to construct the filter function. In this study, we select the S transform to design this polarization filter because its scalable window length can ensure the same number of cycles of a Fourier sinusoid, thereby rendering more precise estimation of local polarization attributes. The results of applying our approach in synthetic and real data examples demonstrate that the proposed polarization filter can effectively attenuate the ground roll and successfully preserve the body wave.     

面波压制的Ridgelet域方法

面波压制是地震数据处理中的一个重要问题. 常规的处理方法虽然能在一定程度上压制面波，但是在处理过程中只是单一的利用面波的一种特性,例如频率域滤波中利用面波与有效信号频率之间的差别，因此难以有效地压制面波. 利用Ridgelet变换可将原始地震记录拓展到（a,τ,p）三维空间，从而可以同时利用地震记录的视速度、时间和尺度域特性差别，实现有效信号与面波的分离. 文中通过理论合成记录及实际地震记录的算例，证实了基于Ridgelet变换的面波压制方法是有效且可行的.     

基于学习型超完备字典的地震数据去噪(英文)

基于变换基函数的方法,是地震去噪处理中最常用的技术之一,它利用地震数据在某种基函数变换域内的稀疏性和可分离性来达到剔除噪声的目的。但传统的做法是事先选定一组固定的变换基并在对应域内进行处理,其效果往往并不十分令人满意。为了探索新的改进方法,我们引入学习型超完备冗余字典,即根据地震模型数据进行学习和训练,以寻求最优的稀疏表示字典,而不是只选用固定的变换基。本文在字典学习中融入全变差最小化策略以压制伪吉布斯现象。我们选用离散傅里叶变换作为初始变换,并以随机噪声为例,对单一的全局变换、未经学习的超完备冗余字典和学习型超完备冗余字典做了比较。结果表明,利用经过训练的超完备冗余字典,在对地震数据进行稀疏表示的同时,也达到了有效去除噪声的目的,可视性和信噪比都得到了明显提高。我们也比较了均匀和不均匀字典子块的效果,结果表明,不均匀的字典子块更利于地震数据去噪。     

基于Dreamlet变换的地震数据压缩理论与方法

为达到更加有效地表示地震数据的目的,仅仅将地震数据当作普通的图像数据处理是远远不够的,地震数据中蕴含的地震波的运动学特性也应作为重要因素而被考虑到.本文讨论了利用Dreamlet变换方法实现地震数据压缩的方法,并针对地震数据本身所蕴含的频散关系特性进一步提出了多尺度Dreamlet变换压缩方法.Dreamlet变换由2个一维局部谐波变换的张量积构成,它在提供地震波场时间-空间局部化性质的同时可以保留波场的运动学特性.通过对二维SEG/EAGE叠前、叠后数据的算例说明了Dreamlet变换用于地震数据压缩的有效性.利用压缩后的数据进行成像的结果更表明,与Curvelet变换方法相比,Dreamlet与多尺度Dreamlet方法可以提供更高的压缩比;在相同压缩比的条件下,使用Dreamlet与多尺度Dreamlet方法压缩重建后的数据进行成像能更好地保留成像结果中的重要结构.     

双稀疏字典和FISTA的地震数据去噪

地震数据的随机噪声去除是地震数据处理中的一项重要步骤,双稀疏字典提供了两层稀疏模型,比单层稀疏模型可以更好地去除噪声.该方法首先利用contourlet变换对地震数据进行稀疏表示,然后在contourlet域中使用快速迭代收缩阈值算法（fast iterative shrinkage-thresholding algorithm,FISTA）对初始字典系数进行更新,接着采用数据驱动紧标架（data-driven tight frame,DDTF）在contourlet域中得到DDTF字典并通过FISTA得到更新后的字典系数,最后通过DDTF字典和更新后的字典系数获得新的contourlet系数,并对新的contourlet系数进行硬阈值和contourlet反变换得到去噪后的数据.通过模拟数据和实际数据的实验证明：与固定基变换去噪方法相比,该方法可以自适应地对地震数据进行稀疏表示,在地震数据较为复杂时得到更高的信噪比;与字典学习去噪方法相比,该方法不仅拥有较快的去噪速度,而且克服了字典学习因为缺少先验约束造成瑕疵的缺点.     

抛物线Radon变换方法在压制地震多次波中的应用

多次波的存在会降低地震资料的信噪比,影响地震资料处理效果和后续的地质解释精度,压制多次波干扰是地震资料处理中的一个重要环节。利用有限差分方法正演模拟几个典型地质模型的地震波场,之后进行动校正,再通过抛物线Radon变换方法把t-x域一次反射波和多次波变换到τ-q域,由于存在速度的差异,Radon域记录中的一次反射波和多次波的能量互相分开,在Radon域对多次波的能量切除,保留反射波的有效信息,达到压制多次波的效果,提高资料的信噪比。通过对实际地震数据进行抛物线Radon变换结果也进一步验证了该方法在压制多次波中的应用效果。      

一种利用多次波求取反射系数的新技术

传统地震资料处理将多次波视为干扰波进行压制,而多次波是有效波在地层中来回传播的反馈,因此它携带了地层反射系数信息。本文从多次波成因出发,推导出反射系数公式(反射系数等于一阶多次波与一次反射波的反褶积)。理论模型实验证明了该方法的正确性。对实际海洋单道地震资料进行处理,求取的反射系数与有效波基本吻合,但由于资料信噪比、子波及反褶积算法等局限性,该技术有待进一步研究。      

局域双曲线Radon变换压制散射波研究与应用

一般而言,由于地下非均质体的存在所产生的二次波源,由它再生成新的波场,叫散射波场。目前,将散射波作为有效波来成像,已开始在溶洞和裂缝等特殊地质体的识别中得到应用。但对于野外资料采集来说,地表复杂地区,如戈壁、砾石区和山前带,大量存在的散射波却是干扰波,它们的存在会严重影响资料的品质,而其研究与实际应用国内外还很少。因此,通过正演模拟,分析散射波的基本特征,在此基础上研究散射波的去噪方法显得十分必要。本文从地震波运动学时距关系出发,研究了反射波和散射波的几何特征;然后用有限差分正演,模拟了散射波场,用理论模型研究并测试了局域双曲线Radon变换散射波去噪新方法。对于实际炮集资料,分析了F-K滤波方法压制散射噪声的局限,采用局域双曲Radon变换有效地去除了炮集中存在的散射噪声,取得了较好的应用效果。     

Sparse representation-based color visualization method for hyperspectral imaging

In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.     

Robust adaptive polarization analysis method for eliminating ground roll in 3C land seismics

To improve the data quality of converted waves, and better identify and suppress the strong ground-roll interference in three-component (3C) seismic recordings on land, we present an adaptive polarization filtering method, which can effectively separate the groundroll interference by combining complex polarization and instantaneous polarization analysis. The ground roll noise is characterized by elliptical plane polarization, strong energy, low apparent velocity, and low frequency. After low-pass filtering of the 3C data input within a given time-window of the ground roll, the complex covariance matrix is decomposed using the sliding time window with overlapping data and length that depends on the dominant ground-roll frequency. The ground-roll model is established using the main eigenvectors, and the ground roll is detected and identified using the instantaneous polarization area attributes and average energy constraints of the ground-roll zone. Finally, the ground roll is subtracted. The threshold of the method is stable and easy to select, and offers good groundroll detection. The method is a robust polarization filtering method. Model calculations and actual data indicate that the method can effectively identify and attenuate ground roll while preserving the effective signals.     

Unsupervised separation of seismic waves using the watershed algorithm on time-scale images

This paper illustrates the use of image processing techniques for separating seismic waves. Because of the non‐stationarity of seismic signals, the continuous wavelet transform is more suitable than the conventional Fourier transforms for the representation, and thus the analysis, of seismic processes. It provides a 2D representation, called a scalogram, of a 1D signal where the seismic events are well localized and isolated. Supervised methods based on this time‐scale representation have already been used to separate seismic events, but they require strong interactions with the geophysicist. This paper focuses on the use of the watershed algorithm to segment time‐scale representations of seismic signals, which leads to an automatic estimation of the wavelet representation of each wave separately. The computation of the inverse wavelet transform then leads to the reconstruction of the different waves. This segmentation, tracked over the different traces of the seismic profile, enables an accurate separation of the different wavefields. This method has been successfully validated on several real data sets.     

Ground roll attenuation based on an empirical curvelet transform

In the field of seismic exploration, ground roll seriously affects the deep effective reflections from subsurface deep structures. Traditional curvelet transform cannot provide an adaptive basis function to achieve a suboptimal denoised result. In this paper, we propose a method based on empirical curvelet transform (ECT) for ground roll attenuation. Unlike the traditional curvelet transform, this method not only decomposes seismic data into multiscale and multi-directional components, but also provides an adaptive filter bank according to frequency content of seismic data itself. So, ground roll can be separated by using this method. However, as the frequency of reflection and ground roll components are close, we apply singular value decomposition (SVD) in the curvelet domain to differentiate the ground roll and reflection better. Examples of synthetic and field seismic data reveal that the proposed method based ECT performs better than the traditional curvelet method in terms of the suppression of ground roll.     

Scattered ground‐roll attenuation using model‐driven interferometry

Scattered ground roll is a type of noise observed in land seismic data that can be particularly difficult to suppress. Typically, this type of noise cannot be removed using conventional velocity‐based filters. In this paper, we discuss a model‐driven form of seismic interferometry that allows suppression of scattered ground‐roll noise in land seismic data. The conventional cross‐correlate and stack interferometry approach results in scattered noise estimates between two receiver locations (i.e. as if one of the receivers had been replaced by a source). For noise suppression, this requires that each source we wish to attenuate the noise from is co‐located with a receiver. The model‐driven form differs, as the use of a simple model in place of one of the inputs for interferometry allows the scattered noise estimate to be made between a source and a receiver. This allows the method to be more flexible, as co‐location of sources and receivers is not required, and the method can be applied to data sets with a variety of different acquisition geometries. A simple plane‐wave model is used, allowing the method to remain relatively data driven, with weighting factors for the plane waves determined using a least‐squares solution. Using a number of both synthetic and real two‐dimensional (2D) and three‐dimensional (3D) land seismic data sets, we show that this model‐driven approach provides effective results, allowing suppression of scattered ground‐roll noise without having an adverse effect on the underlying signal.     

SV波全反射作用下地震动的合成及振动特性

越来越多的研究表明来自基岩的地震波并不是垂直地面向上传播的。地震波在斜入射与垂直入射时所产生的场地效应有很大不同,由于存在全反射现象,SV波在斜入射时产生的场地效应更为复杂。文章基于均匀弹性半空间地震波传播理论,分别推导得到SV波入射角在小于、大于等于临界角时地震动的计算表达式,通过模型算例研究SV波全反射作用下的地震动特性。研究发现：由SV波产生的地震动主要由入射波和反射波构成,滑行波作用可以忽略;地面运动轨迹具有面波旋转振动特点;随着入射角的增大,地面震动从以水平方向振动为主逐渐过渡到以垂直方向振动为主,两个方向的振动均小于入射波峰值的2倍。     

Two‐dimensional dictionary learning for suppressing random seismic noise

Dictionary learning is a successful method for random seismic noise attenuation that has been proven by some scholars. Dictionary learning–based techniques aim to learn a set of common bases called dictionaries from given noised seismic data. Then, the denoising process will be performed by assuming a sparse representation on each small local patch of the seismic data over the learned dictionary. The local patches that are extracted from the seismic section are essentially two‐dimensional matrices. However, for the sake of simplicity, almost all of the existing dictionary learning methods just convert each two‐dimensional patch into a one‐dimensional vector. In doing this, the geometric structure information of the raw data will be revealed, leading to low capability in the reconstruction of seismic structures, such as faults and dip events. In this paper, we propose a two‐dimensional dictionary learning method for the seismic denoising problem. Unlike other dictionary learning–based methods, the proposed method represents the two‐dimensional patches directly to avoid the conversion process, and thus reserves the important structure information for a better reconstruction. Our method first learns a two‐dimensional dictionary from the noisy seismic patches. Then, we use the two‐dimensional dictionary to sparsely represent all of the noisy two‐dimensional patches to obtain clean patches. Finally, the clean patches are patched back to generate a denoised seismic section. The proposed method is compared with the other three denoising methods, including FX‐decon, curvelet and one‐dimensional learning method. The results demonstrate that our method has better denoising performance in terms of signal‐to‐noise ratio, fault and amplitude preservation.     

小波多分辨分析方法在波动方程正演模拟中的应用

初步探讨小波多分辨分析理论在地震波动方程正演模拟中的应用，给出一种基于地震波场局部变化性质而自适应调空间网格点的波动方程数值算法，目前求解波动方程所用的有限差分法、有限元法以及伪谱法都不能根据波场的局部变化性质而动态选择空间网格点的大小、小波基函数在空间域和频域中都具有局部性特征，它的性质优于有限差分法，有限元法和伪谱法中所用的基函数，通过阀值运算，地震波场失发辨表示变得非常稀疏，同时地下介质中的主要信息又不会受到损害，本文将声波场的多分辨表示变得非常稀疏，同时地下介质中的主要信息又不会受到损害，本文将声波方程的矩阵表示形式小波多分辨分析的框架下进行了展开，通过对算子长阵的地震波场矩阵进行多分辨分解和压缩，得到了小波域中地震波场正演模拟算法。     

Seismic recognition of local inhomogeneities

A simple method to contour local inhomogeneities using seismic data is proposed. It formalizes an approximate inversion method which is based on the interpretation of local inhomogeneities as making the differences between an actual seismic data set and a previous reference model. It uses the optimal statistical criteria of parameter estimation and recognition and the ray representation of the waves spreading. Any combination of direct, reflected and/or other types of waves may be used as the database. Inhomogeneities, having a size two times above the wavelength of the seismic waves, can be resolved. Laboratory experiments, using ultrasonic waves and analysis of data from field experiments, confirmed the theoretical results. The method can be used to search for ore bodies, kimberlite cubes, oiltraps, etc.     

不同尺度地形的SH波频率域响应特征研究

地形是影响实际地震记录的一个重要因素,理论计算已证明地形影响地震波的走时、能量、震相、波形等特征,同时地形也是产生尾波信号的重要原因.复杂且具有多尺度成分的地形对地震波传播的影响不仅与地震信号的频率有关,与其自身尺度成分也表现出强烈的依赖关系. 本文利用局域离散波数法模拟计算了不同尺度地形SH波的频率响应,得到地形尺度与频率之间似共振的响应关系. 这一关系可以更好地解释复杂地形对不同频率地震波传播的影响方式,同时可以指导数值模拟模型的构建,在关注的频率范围内合理取舍构造尺度成分,达到以最小的计算量得到最大的计算效益.