SOLUTION OF THE VSP ONE-DIMENSIONAL INVERSE PROBLEM*

The acoustical impedance distribution of the substratum, or equivalently, the reflection coefficient sequence, is determined from VSP data. This nonlinear inverse problem is solved by a least-squares method. As the wavelet is unknown, the impedance distribution and the Neumann boundary condition (which characterizes the excitation of the medium) are simultaneously identified. The inversion method is applied to synthetic and field VSP's; the result is satisfactory, even when strong noise corrupts the data, provided that a suitable constraint on the impedance distribution is introduced in order to ensure the stability of the inverse problem. The reliability of the inversion result in the case of field VSP, is confirmed. Some ways in which this result may be used are illustrated (calibration of the seismic surface data, multiple identification, prediction ahead of the bit).     

最小二乘傅立叶有限差分偏移

一般偏移算法是用反演算子通过解析方法求解.最小二乘偏移方法采用另一种思路,即采用数值方法,通过解一个线性离散反问题来索求解.这样我们试着寻找一个模型匹配地震数据并能表现出其某些特点来得到偏移图像.最小二乘法能减少偏移赝像,得到更精确的偏移效果.Kirchhoff算子在最小二乘偏移方法中应用较广,但需要较多的迭代次数,而且具有Kirchhoff偏移的缺点.本文把最小二乘方法运用到基于波长延拓的波动方程偏移方法中,为提高最小二乘偏移的效率,可采用效率较高的正传播算子和反传播算子.我们利用效率较高,能适应剧烈横向变速的傅立叶有限差分正传播和反传播算子来做叠后最小二乘偏移.数值实例表明,通过少数的共轭梯度法迭代,就能得到与真实模型差别很微小的偏移效果.对于傅式变换我们采用了数值软件FFTW,其变换速度比常规FFT算法一般要快六倍以上,进一步提高了效率.本文算法很容易在并行机上实现,这些特点在处理大型数据时大有裨益.     

位移和加速度融合的自适应多速率Kalman滤波方法

多速率Kalman滤波方法可用于低采样率的位移和高采样率的加速度数据融合,而未知的噪声协方差信息则显著制约着多速率Kalman滤波精度.本文通过将多速率Kalman滤波转换为传统的单速率Kalman滤波,建立了Kalman滤波增益的自协方差矢量与未知的加速度谱密度和观测噪声参数间的线性函数模型,并采用最小二乘估计方法对未知的噪声协方差参数进行估计,进而有效地提高了多速率Kalman滤波精度.数值仿真和震动台实验结果验证了本文方法的正确性和有效性.     

Imaging of elastic seismic data by least-squares reverse time migration with weighted L2-norm multiplicative and modified total-variation regularizations

Least-squares reverse time migration has the potential to yield high-quality images of the Earth. Compared with acoustic methods, elastic least-squares reverse time migration can effectively address mode conversion and provide velocity/impendence and density perturbation models. However, elastic least-squares reverse time migration is an ill-posed problem and suffers from a lack of uniqueness; further, its solution is not stable. We develop two new elastic least-squares reverse time migration methods based on weighted L2-norm multiplicative and modified total-variation regularizations. In the proposed methods, the original minimization problem is divided into two subproblems, and the images and auxiliary variables are updated alternatively. The method with modified total-variation regularization solves the two subproblems, a Tikhonov regularization problem and an L2-total-variation regularization problem, via an efficient inversion workflow and the split-Bregman iterative method, respectively. The method with multiplicative regularization updates the images and auxiliary variables by the efficient inversion workflow and nonlinear conjugate gradient methods in a nested fashion. We validate the proposed methods using synthetic and field seismic data. Numerical results demonstrate that the proposed methods with regularization improve the resolution and fidelity of the migration profiles and exhibit superior anti-noise ability compared with the conventional method. Moreover, the modified-total-variation-based method has marginally higher accuracy than the multiplicative-regularization-based method for noisy data. The computational cost of the proposed two methods is approximately the same as that of the conventional least-squares reverse time migration method because no additional forward computation is required in the inversion of auxiliary variables.     

Note on the determination of the best-fitting plane for a given set of directions

Summary Using Lagrange's multiplicator, a least-squares solution for the position of the plane that is in best accord with a finit number of directions is derived. Reference is made to a seismo-tectonic problem to which the solution can be applied.     

基于非二次幂Curvelet变换的最小二乘匹配算法及其应用

本文提出了基于非二次幂Curvelet变换的最小二乘匹配算法.首先,根据输入地震信号的频谱和方向等特征进行非二次幂Curvelet变换,根据其特征不同,最大程度地将有效信号和噪声分开;然后,在噪声能量集中的非二次幂Curvelet子记录上对输入数据和预测的噪声模型进行最小二乘匹配滤波处理.本方法提高了常规最小二乘匹配算法在时间空间域内进行信噪分离的稳定性和准确性.对含有面波的实际地震数据进行测试,其结果表明本方法可以有效地压制面波干扰,特别是当面波和有效信号有交叉或重叠等现象出现时,能较好地保护反射同相轴信息.本方法还可用于对含自由表面多次波和层间多次波等地震数据进行自适应信噪分离.     

Spatio-temporal filtering using wavelets

 In this paper, a class of spatio-temporal processes with first-order autoregressive temporal structure and functional spatio-temporal interaction is introduced. The spatial second-order regularity is allowed to change over time and is characterized in terms of fractional Sobolev spaces. The associated filtering problem is considered, assuming that observations are defined by spatial linear functionals of the process of interest, being affected by additive noise. Conditions under which a stable solution to this problem is obtained are studied. A functional least-squares linear estimate fusion method is derived to calculate this solution A multiscale finite-dimensional approximation to the problem is obtained from the wavelet-based orthogonal expansions of the time cross-section spatial processes, which allows the numerical inversion of the linear operator involved.     

Quantitative estimate of VTI parameters from AVA responses

The influence of the vertical transverse isotropy (VTI) on amplitude versus angle (AVA) responses is first studied on the linearized formula of the PP-reflection coefficient. Up to medium angles of incidence, as in the isotropic case, only two quantities can be retrieved, the second with less accuracy than the first. These quantities are the P-impedance and the S-impedance multiplied by 1− δ/2, where δ is one of the two anisotropic parameters introduced by Thomsen. To extend these results to the exact formulae, the AVA analysis is then formulated as an inverse problem and a least-squares cost function is defined. A study of the eigenvalues and eigenvectors of the Hessian of the least-squares cost function confirms these results. Though these results are dependent on the amount of data and on the maximum angle of incidence available, they are appropriate for small and medium angles of incidence. Thanks to this inverse formulation, this work can be extended to the case of multicomponent AVA responses. The addition of PS-reflection data further constrains the problem, but the S-impedance and δ are still coupled. However, the addition of SS-reflection data gives an estimation of both P- and S-impedances and δ. The last two parameters, the density and the second anisotropic parameter ɛ, remain difficult to determine, at least with small-to-medium angular apertures.     

Least-squares Minimization Approaches to Interpret Total Magnetic Anomalies Due to Spheres

We have developed three different least-squares approaches to determine successively: the depth, magnetic angle, and amplitude coefficient of a buried sphere from a total magnetic anomaly. By defining the anomaly value at the origin and the nearest zero-anomaly distance from the origin on the profile, the problem of depth determination is transformed into the problem of finding a solution of a nonlinear equation of the form f(z)=0. Knowing the depth and applying the least-squares method, the magnetic angle and amplitude coefficient are determined using two simple linear equations. In this way, the depth, magnetic angle, and amplitude coefficient are determined individually from all observed total magnetic data. The method is applied to synthetic examples with and without random errors and tested on a field example from Senegal, West Africa. In all cases, the depth solutions are in good agreement with the actual ones.     

同时震源数据的加权结构增强最小二乘逆时偏移

同时震源（Simultaneous-source,SS）地震采集技术能有效地提高地震数据采集效率,但直接对SS混合数据偏移成像会在最后的成像剖面中引入很强的串扰噪声.将SS数据偏移成像看作一个反演问题,利用最小二乘（Least-squares,LS）求解是压制SS直接成像中串扰噪声的一种有效尝试.构造增强滤波（Structure-enhancing filter,SE）约束的最小二乘逆时偏移（LSRTM）方法可以有效地压制SS数据成像中的串扰噪声,但SE实质为低通滤波,会将成像中的陡倾角等细节信息平滑涂抹,降低成像分辨率.本文在利用SE对LSRTM约束的基础上,提出了基于加权构造增强约束的LSRTM方法（WSE-LSRTM）并应用于SS数据的反演成像中.该方法不仅能够有效地压制串扰噪声（cross-talk）、保留结构信息,而且可以保护成像中的陡倾角结构不被过度平滑而破坏.在对简单模型和复杂Marmousi模型的数值测试中,该方法都取得了良好的效果.     

A three-stage identification approach for hysteretic systems

This paper deals with the identification of the parameters of a smoothed hysteretic model which was proposed by Bouc and Wen with emphasis on restoring force hysteresis. The problem of estimating the parameters of this system on the basis of input-output data, possibly noise corrupted, is considered. Through the application of various simulated time histories from the hysteretic model, a three-stage systematic method of system identification was proposed. Four different methods of identification are arranged and conducted in this three-stage system identification. The first stage, a sequential regressional analysis is used to identify the equivalent linear system from which elastic or inelastic response can be identified. The identified parameters can be used in the stage when the system is in elastic response. In the second stage, both time domain least-squares method and Gauss-Newton method are applied. The convergence of the Gauss-Newton method can be guaranteed if the identified results from least-squares method are adopted as the initial values for Gauss-Newton method. In the third stage, the extended Kalman filtering technique is needed to identify the noise-corrupt data. Application of this algorithm to a SDOF non-deteriorating system is verified.     

基于照明补偿的单程波最小二乘偏移

最小二乘偏移是一种基于反射地震数据与地下反射率间线性关系而建立起来的地震数据线性反演方法,相比常规偏移成像具有更好的保幅性能.本文提出了一种基于照明补偿的单程波最小二乘偏移方法,首先利用单程波方程的稳定Born近似广义屏波场传播算子构建反射地震数据与地下反射率间的线性算子,然后再应用线性最优化方法求解最小二乘偏移所对应的线性反问题.在迭代求解最优化问题的过程中,以地震波场的地下照明强度作为迭代反演的预条件算子加快迭代的收敛速度.单程波传播过程中考虑了速度分界面产生的透射效应,并用单极震源代替常规偏移中的偶极震源.把本文提出的方法应用于层状理论模型和Marmosi模型地震数据的数值试验中均取得了理想的结果.     

MAXIMUM-LIKELIHOOD ESTIMATION OF SEISMIC IMPULSE RESPONSES*

A seismic trace is assumed to consist of a known signal pulse convolved with a reflection coefficient series plus a moving average noise process (colored noise). Multiple reflections and reverberations are assumed to be removed from the trace by conventional means. The method of maximum likelihood (ML) is used to estimate the reflection coefficients and the unknown noise parameters. If the reflection coefficients are known from well logs, the seismic pulse and the noise parameters can be estimated. The maximum likelihood estimation problem is reduced to a nonlinear least-squares problem. When the further assumption is made that the noise is white, the method of maximum likelihood is equivalent to the method of least squares (LS). In that case the sampling rate should be chosen approximately equal to the Nyquist rate of the trace. Statistical and numerical properties of the ML- and the LS-estimates are discussed briefly. Synthetic data examples demonstrate that the ML-method gives better resolution and improved numerical stability compared to the LS-method. A real data example shows the ML- and LS-method applied to stacked seismic data. The results are compared with reflection coefficients obtained from well log data.     

基于保角变换的双谱域相位谱估计方法及其在地震子波估计中的应用(英文)

地震子波估计是地震资料处理与解释中的重要环节,它的准确与否直接关系到反褶积及反演等结果的好坏。高阶谱(双谱和三谱)地震子波估计方法是一类重要的、新兴的子波估计方法,然而基于高阶谱的地震子波估计往往因为高阶相位谱卷绕的原因,导致子波相位谱求解产生偏差,进而影响了混合相位子波估计的效果。针对这一问题,本文在双谱域提出了一种基于保角变换的相位谱求解方法。通过缩小傅里叶相位谱的取值范围,有效避免了双谱相位发生卷绕的情况,从而消除了原相位谱估计中双谱相位卷绕的影响。该方法与最小二乘法相位谱估计相结合,构成了基于保角变换的最小二乘地震子波相位谱估计方法,并与最小二乘地震子波振幅谱估计方法一起,应用到了地震资料混合相位子波估计中。理论模型和实际资料验证了该方法的有效性。同时本文将双谱域地震子波相位谱估计中保角变换的思想推广到三谱域地震子波相位谱估计中。     

COMPUTING THE KERNEL FUNCTION IN RESISTIVITY SOUNDING WITH AN ARBITRARY ELECTRODE CONFIGURATION*

A power series expansion can be used to obtain the kernel from apparent resistivities for an arbitrary electrode configuration. Three types of function are most appropriate for this purpose. The expansion coefficients can be by a least-squares method. In this case, ortho-normalization of the functions is of great advantage. An example with the Wenner configuration is given.     

Shortcomings in the determination of regional fields by polynomial fitting: A simple solution

The procedure of polynomial fitting by least-squares technique to obtain the regional field from potential field data is commonly used in processing geophysical data. However, it has been found that the unguarded use of this procedure with measured field data may lead to erroneous regional fields. This is due to the fact that the least-squares technique pre-supposes a random variation of the residual or anomaly field. This condition is rarely fulfilled in measured field data where there is the common occurrence of ‘deviatory field points’ in the residual field data. In this paper, a procedure to overcome the problem using robust statistics is described with the straight-forward Downhill Simplex method used to obtain the optimum coefficients for the polynomial. The method is illustrated with aeromagnetic field data from the Mamfe basin of Nigeria and Cameroon. The result shows that the trend of the regional field obtained from robust statistics is consistent with the trend in the aeromagnetic field obtained by using the method of cross-correlation trend analysis.     

裂步法最小二乘偏移

最小二乘偏移的核心思想是用正向传播算子的逆算子代替其共轭转置算子进行偏移.它克服了常规偏移方法用共轭转置算子偏移的缺点.本文详细地描述了裂步法最小二乘偏移的原理,实现算法,并通过两个模型展示了裂步法最小二乘偏移的可行性和有效性.并且通过研究发现最小二乘较容易被扩展到基于其它波场延拓算子的最小二乘偏移.     

SIGNAL-TO-NOISE RATIO ENHANCEMENT IN MULTICHANNEL SEISMIC DATA VIA THE KARHUNEN-LOÉVE TRANSFORM*

The Karhunen-Loéve transform, which optimally extracts coherent information from multichannel input data in a least-squares sense, is used for two specific problems in seismic data processing. The first is the enhancement of stacked seismic sections by a reconstruction procedure which increases the signal-to-noise ratio by removing from the data that information which is incoherent trace-to-trace. The technique is demonstrated on synthetic data examples and works well on real data. The Karhunen-Loéve transform is useful for data compression for the transmission and storage of stacked seismic data. The second problem is the suppression of multiples in CMP or CDP gathers. After moveout correction with the velocity associated with the multiples, the gather is reconstructed using the Karhunen-Loéve procedure, and the information associated with the multiples omitted. Examples of this technique for synthetic and real data are presented.     

El Niño Impact on Polar Motion Prediction Errors

The polar motion prediction is computed as a least-squares extrapolation of the polar motion data. The least-squares model consists of a Chandler circle with constant or variable amplitude, annual and semiannual ellipses, and a bias. The model with constant amplitude of the Chandler oscillation is fit to the last three years of polar motion data and the model with variable amplitude of the Chandler oscillation is fit to the whole time series ranging from 1973.0 to 2001.1. The variable amplitude of the Chandler oscillation is modeled from the envelope of the Chandler oscillation filtered by the Fourier transform band pass filter from the long-term IERS EOPC01 polar motion series. The accuracy of the polar motion prediction depends mostly on the phase variation of the annual oscillation, which is treated as a constant in the least-squares adjustment. There were two significant changes of the annual oscillation phase of the order of 30° before the two El Niño events in 1982/83 and 1997/98.     

Addressing non-uniqueness in linearized multichannel surface wave inversion

The multichannel analysis of the surface waves method is based on the inversion of observed Rayleigh-wave phase-velocity dispersion curves to estimate the shear-wave velocity profile of the site under investigation. This inverse problem is nonlinear and it is often solved using 'local' or linearized inversion strategies. Among linearized inversion algorithms, least-squares methods are widely used in research and prevailing in commercial software; the main drawback of this class of methods is their limited capability to explore the model parameter space. The possibility for the estimated solution to be trapped in local minima of the objective function strongly depends on the degree of nonuniqueness of the problem, which can be reduced by an adequate model parameterization and/or imposing constraints on the solution.
In this article, a linearized algorithm based on inequality constraints is introduced for the inversion of observed dispersion curves; this provides a flexible way to insert a priori information as well as physical constraints into the inversion process. As linearized inversion methods are strongly dependent on the choice of the initial model and on the accuracy of partial derivative calculations, these factors are carefully reviewed. Attention is also focused on the appraisal of the inverted solution, using resolution analysis and uncertainty estimation together with a posteriori effective-velocity modelling. Efficiency and stability of the proposed approach are demonstrated using both synthetic and real data; in the latter case, cross-hole S-wave velocity measurements are blind-compared with the results of the inversion process.