A seismic interpolation and denoising method with curvelet transform matching filter

A new seismic interpolation and denoising method with a curvelet transform matching filter, employing the fast iterative shrinkage thresholding algorithm (FISTA), is proposed. The approach treats the matching filter, seismic interpolation, and denoising all as the same inverse problem using an inversion iteration algorithm. The curvelet transform has a high sparseness and is useful for separating signal from noise, meaning that it can accurately solve the matching problem using FISTA. When applying the new method to a synthetic noisy data sets and a data sets with missing traces, the optimum matching result is obtained, noise is greatly suppressed, missing seismic data are filled by interpolation, and the waveform is highly consistent. We then verified the method by applying it to real data, yielding satisfactory results. The results show that the method can reconstruct missing traces in the case of low SNR (signal-to-noise ratio). The above three problems can be simultaneously solved via FISTA algorithm, and it will not only increase the processing efficiency but also improve SNR of the seismic data.     

OPTIMIZATION OF SHORT DIGITAL LINEAR FILTERS FOR INCREASED ACCURACY*

The accuracy of short length digital linear filter operators can be substantially increased if the sampling interval as well as the abscissa shift are properly adjusted. This may be done by a trial and error process of adjustment of these parameters until the error made by the filter operator, applied to a suitably chosen test function, is smallest. As an illustration of the application of this method, 7-, 11- and 19-point filters for the calculation of Schlumberger apparent resistivity from a known resistivity transform are designed. Errors with the new 7-point filter are seen to be less than those with a 19-point filter of conventional design. The errors with the new 19-point filter are two to three orders of magnitude smaller than those made by the conventional 19-point filter. The new method should provide digital linear operators that allow significant improvements in accuracy for comparable computation efforts, or substantial reduction in computation for comparable accuracy of results, or something of both.     

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.     

PROGRESS IN THE DIRECT INTERPRETATION OF RESISTIVITY SOUNDINGS: AN ALGORITHM*

An algorithm is presented for the direct interpretation of resistivity sounding data. The algorithm is based on the method of successive reductions to lower boundary plane of the resistivity transform function. A novel aspect of the algorithm is that error limits are assigned to the initial values of the resistivity transform, and these error limits are carried through in all the subsequent computations. The width of the error range is then used as the basis for assigning weight factors in the final computation of thicknesses and resistivities of the layers. The errors in the resistivity transform derived from the solution given by the algorithm are usually not more than twice as large as those in the original data.     

拾振器输入信号恢复方法的研究

为了补偿由于拾振器及其测量系统有限带宽而使测量结果产生的测量误差，本文提出了一种用FIR横向滤波器来恢复拾振器原始输入信号的方法。该方法是将拾振器的输出信号作为FIR横向滤波器的输入，用自适应RLS算法对FIR横向滤波器系数进行辨识而最终获得可以恢复拾振器原始输入信号的反卷积滤波器。通过用几组不同特点的信号对其进行实验检验，结果证明该方法的有效性和较好的泛化能力。     

小波包分解树结点与信号子空间频带的对应关系及其应用

小波包变换的Mallat分解算法可以把较宽的信号频带划分成相等带宽且互不重叠的窄频带,但由于信号子空间频带的频率大小并非按照分解树结点（node）编号的大小顺序排列,各个结点重构信号的频率范围不易判定. 本文通过分析小波包变换的Mallat分解算法与分解滤波器的关系,设定频带编号与结点编号间进行二进制转化的运算规则,得到了小波包分解树结点与信号子空间频带的对应关系,然后通过模拟信号进行了验证. 结果表明,本文给出的小波包信号子空间频带的排列规则是正确的.      

ZERO—PHASE FORWARD FILTERS FOR RESISTIVITY SOUNDING*

Forward filters to transform the apparent resistivity function over a layered half-space into the resistivity transform have been derived for a number of sample intervals. The filters have no apparent Gibbs' oscillations and hence require no phase shift. In addition, the end points of the filter were modified to compensate for truncation. The filters were tested on simulated ascending and descending two-layer cases. As expected, “dense” filters with sample spacing of In (10)/6 or smaller performed very well. However, even “sparse” filters with spacing of In (10)/2 and a total of nine coefficients have peak errors of less than 5% for p₁:p₂ ratios of 10^–6 to 10⁶. If a peak error of 5.5% is acceptable, then an even sparser filter with only seven coefficients at a spacing of 3 In (10)/5 may be used.     

Diffraction imaging by prestack reverse‐time migration in the dip‐angle domain

Prestack image volumes may be decomposed into specular and non‐specular parts by filters defined in the dip‐angle domain. For space‐shift extended image volumes, the dip‐angle decomposition is derived via local Radon transform in depth and midpoint coordinates, followed by an averaging over space‐shifts. We propose to employ prestack space‐shift extended reverse‐time migration and dip‐angle decomposition for imaging small‐scale structural elements, considered as seismic diffractors, in models with arbitrary complexity. A suitable design of a specularity filter in the dip‐angle domain rejects the dominant reflectors and enhances diffractors and other non‐specular image content. The filter exploits a clear discrimination in dip between specular reflections and diffractions. The former are stationary at the specular dip, whereas the latter are non‐stationary without a preferred dip direction. While the filtered image volume features other than the diffractor images (for example, noise and truncation artefacts are also present), synthetic and field data examples suggest that diffractors tend to dominate and are readily recognisable. Averaging over space‐shifts in the filter construction makes the reflectors? rejection robust against migration velocity errors. Another consequence of the space‐shift extension and its angle‐domain transforms is the possibility of exploring the image in a multiple set of common‐image gathers. The filtered diffractions may be analysed simultaneously in space‐shift, scattering‐angle, and dip‐angle image gathers by means of a single migration job. The deliverables of our method obviously enrich the processed material on the interpreter's desk. We expect them to further supplement our understanding of the Earth's interior.     

Corresponding relationships between nodes of decomposition tree of wavelet packet and frequency bands of signal subspace

Mallat decomposition algorithm of wavelet packet transform can divide broader band into narrower ones with equal bandwidth and no overlapping each other. However, order by size of frequency within signal subspace is not in accordance with that of node label of decomposition tree. Therefore, it is not easy to determine frequency range of reconstructed signal from each node. In this paper, by analyzing the relationship between Mallat decomposition algorithm of wavelet packet transform and decomposition filter and setting operation rule of binary conversion of band labels into node labels, we find the corresponding relationships between nodes of decomposition tree and frequency bands of signal subspace. Then, the conclusion is verified by analog signals. Also, it shows that the ar- ranged rule of bands of signal subspace summarized in the paper is correct.     

Identification of non-linear and non-stationary soil properties during the 1995 Hyogoken-nanbu earthquake

An extended Kalman filter algorithm with local iteration is presented for the identification of non-linear and non-stationary soil properties. Borehole-array strong motions were recorded at a liquefied site during the 1995 Hyogoken-nanbu earthquake. In this study, a modified Kalman filtering method in which the extended Kalman filter is iteratively used at every local time-step to track rapid parameter changes is proposed. The method is then applied to the instrumented soil layer, which is modeled by an equivalent linear model. An identification of non-linear and non-stationary soil properties was conducted successfully; and non-linear restoring force–displacement relationships including progression with time were obtained.     

Obtaining estimates of the low-frequency ‘fling’, instrument tilts and displacement timeseries using wavelet decomposition

This paper proposes a novel, wavelet-based algorithm, which by extracting the low-frequency fling makes it possible to automatically correct for baseline shift and re-integrate down to displacement. The algorithm applies a stationary-wavelet transform at a suitable level of decomposition to extract the low frequency fling model in the acceleration time histories. The low frequency, acceleration fling should be as close as possible to the theoretical type A model, which after correction leads to a pulse-type velocity and ramp-like displacement after first and second integration. The wavelet transform essentially decomposes the seismic record using maximally flat filters and these together with a de-noising scheme form the core of this approach, which is to extract the lower and higher frequency sub-band acceleration, velocity and displacement profiles and correct for baseline shift. The correction automatically selects one time point from the low-frequency sub-band and then zeros the acceleration baseline after the fling. This implies pure, translation without any instrument tilts. Estimates of instrument tilt angles are also obtainable from the wavelet transformed time history as well as estimates of signal-to-noise ratios. The acceleration data used in this study is from station TCU068 in the near-fault region of the Chi-Chi, Taiwan, earthquake of 20th September 1999.     

Nonlinear data processing method for the signal enhancement of GPR data

An alternative data processing procedure is proposed in this paper for the purpose of enhancing the signal/noise (S/N) ratio of ground penetrating radar (GPR) data. The processing methodology is achieved by performing the logarithmic transform in conjunction with the ensemble empirical mode decomposition (EEMD), a new nonlinear data analysis method in signal processing. The synthetic model study and field example indicate that the logarithmic transform is effective in alleviating the attenuation problem. Additionally, the spectrogram obtained from Hilbert-Huang transform (HHT) shows that the decomposition sensitivity of the EEMD method is greatly improved with the aid of the logarithmic transform. This new method allows us to extract the signal components from noisy GPR data efficiently. The success of this study suggests a possible nonlinear analysis application in future GPR investigation, particularly in the filter design and gain correction.     

Estimation of Seismic Velocities from Deep Reflections in the τ-p Domain

—In deep reflection seismics the estimation of seismic velocities is hampered in most cases due to the low signal level with respect to noise. In the τ-p domain, it is possible to perform the velocity analysis even under such unfavorable signal conditions. This is achieved by making use of special properties of the transform, which enhance the signal-to-noise ratio. Further noise suppression is realized by incorporating filter procedures into the transform algorithm. The velocity analysis itself is also done in the τ-p domain by calculating and evaluating constant velocity gathers. The results can be directly used in the time domain. A mute algorithm, implemented into the τ-p velocity analysis procedure, further reduces noise. This velocity estimation method is discussed with synthetic data and applied to DEKORP data.     

利用小波变换提取面波群速度

选取了几种常见的小波母函数,分别提取了同一理论下的面波数据的群速度,并与理论群速度进行对比,结果表明Morlet小波提取面波群速度的效果最好.此外,将Morlet小波与常用的多重滤波提取群速度的结果进行了比较,结果表明: ① 多重滤波法非常依赖高斯滤波系数α的取值,α的取值应随面波周期的增大而减小;② 在α取值得当的前提下,在20—35 s周期范围内多重滤波法提取面波群速度的相对误差比Morlet小波小,在周期大于35 s时,两者相对误差相近; ③ 合适的α值的选取需在不同周期段耗费大量时间进行大量试验,这说明多重滤波法不具备自适应性;而采用小波变换分析短周期信号时,时间窗变窄,频率窗变长,当分析长周期信号时,时间窗变长,频率窗变窄,具有对信号的自适应性,这是小波变换相比多重滤波法的最大优点.      

S变换在面波去噪中的应用r

S变换是一种用于分析非平稳信号的时频变换方法, 可以很好地刻画地震信号的时频特性. 本文将S变换用于地震面波数据的噪声去除中, 首先介绍了S变换的理论基础, 然后设计了时频滤波和阈值滤波两种方法, 分别对天然地震面波数据和背景噪声数据进行去噪处理, 并与相位匹配滤波进行了比较． 结果表明, 面波数据经S变换去噪后, 群速度频散曲线的短周期部分得到改善, 能够连续追踪至6 s左右, 但长周期部分出现了缺失; S变换去噪的效果优于相位匹配滤波, 两者相结合会得到更加理想的结果．      

表面多次波衰减的研究现状与进展

表面多次波是一种相干干扰,在海洋地球物理勘探中表面多次波尤为明显.本文给出了表面多次波衰减方法的研究现状的系统阐述,并分析了表面多次波衰减研究的发展趋势.表面多次波衰减主要有滤波法和波动理论方法.对于滤波法给出了广泛应用的抛物Radon变换法的实例,指出了抛物Radon 变换法存在滤波时能量泄漏的问题.同时本文分析了基于波动理论的处理优势和存在的问题,该方法可以处理任意介质的情形,但是对于数据的采集要求比较严格,另外在进行表面多次波衰减处理以前,要进行一系列的预处理,这些预处理的误差直接决定着多次波衰减的效果.最后,本文给出了表面多次波衰减的发展趋势——将滤波法和波动理论法结合来实现多次波的衰减.两类方法如何有效地结合是该类方法一个重要的研究方向.     

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.     

基于非均匀快速傅里叶变换的最小二乘反演地震数据重建

不规则采样地震数据的重建是地震数据分析处理的重要问题.本文给出了一种基于非均匀快速傅里叶变换的最小二乘反演地震数据重建的方法,在最小二乘反演插值方程中,引入正则化功率谱约束项,通过非均匀快速傅里叶变换和修改周期图的方式,自适应迭代修改约束项,使待插值数据的频谱越来越接近真实的频谱,采用预条件共轭梯度法迭代求解,保证了解的稳定性和收敛速度.理论模型和实际地震数据插值试验证明了本文方法能够去除空间假频,速度快、插值效果好,具有实用价值.     

Multiscale anisotropic diffusion for ringing artifact suppression in geophysical deconvolution data

Ringing artifact degradations always appear in the deconvolution of geophysical data. To address this problem, we propose a postprocessing approach to suppress ringing artifacts that uses a novel anisotropic diffusion based on a stationary wavelet transform (SWT) algorithm. In this paper, we discuss the ringing artifact suppression problem and analyze the characteristics of the deconvolution ringing artifact. The deconvolution data containing ringing artifacts are decomposed into different SWT subbands for analysis, and a new multiscale adaptive anisotropic filter is developed to suppress these degradations. Finally, we demonstrate the performance of the proposed method and describe the experiments in detail.     

基于复曲波变换的一次波和多次波分离方法

多次波压制方法的研究一直都是地震数据处理中非常重要的一个课题.由于常用的多次波匹配方法主要针对多次波模型和实际多次波存在的振幅或相位的差异进行匹配校正,而无法直接校正多次波模型和实际多次波存在时移误差.本文构建了一种复曲波变换的算法,利用复曲波变换的时移不变性质,通过调整复曲波系数的振幅和相位实现对多次波模型振幅和时移误差的校正.为了更好地保护有效信号,在一次波和多次波分离前,引入一个非线性屏蔽滤波器,可以事先分离出大部分有效波,然后再将剩余部分数据作为输入数据,在复曲波域进行剩余一次波和多次波分离.最后通过模型试算和实际资料处理验证了本文提出的一次波和多次波分离方法的有效性.