L1范数约束被动源数据稀疏反演一次波估计

对于被动源地震数据,运用常规的互相关算法得到的虚拟炮记录中,不仅含有一次波反射信息,还包括了表面相关多次波.然而,通过传统的被动源数据稀疏反演一次波估计(EPSI)方法,可以求得只含有一次波,不含表面相关多次波的虚拟炮记录.本文改进了传统的被动源数据稀疏反演一次波估计问题的求解方法,将被动源稀疏反演一次波估计求解问题转化为双凸L1范数约束的最优化求解问题,避免了在传统的稀疏反演一次波估计过程中用时窗防止反演陷入局部最优化的情况.在L1范数约束最优化的求解过程中,又结合了2DCurvelet变换和小波变换,在2DCurvelet-wavelet域中,数据变得更加稀疏,从而使求得的结果更加准确,成像质量得到了改善.通过简单模型和复杂模型,验证了本文提出方法的有效性.     

储层重力密度反演后验约束正则化方法

本文针对蒸汽辅助重力泄油(SAGD)生产中开发监测问题,发展了综合应用地震及重力数据反演储层密度的联合反演算法.通过测井数据建立纵波阻抗与密度的直接关系,并推导出这种关系下重力与纵波阻抗数据联合反演的计算方法,从而计算出蒸汽腔体密度分布规律.文中应用密度反演后验约束正则化方法,采用Tikhonov正则化模型,通过波阻抗数据作为约束进行联合反演,在算法上提高了稳定性,同时得到较高的反演精度.文中对SAGD生产中的理论模型进行了方法试算,并分析了算法的误差,最终应用于SAGD生产的实际数据中,通过最终反演结果分析,该方法取得了很好的应用效果.     

基于空变阻抗约束的贝叶斯稀疏脉冲反演

常规的基于贝叶斯理论的稀疏脉冲反演中,各约束项的拉格朗日算子均采用的是恒定的常系数。反演实际资料发现,波阻抗反演剖面与钻井资料的油气显示并不能很好地对应。考虑到反演不同地震道数据时,低频趋势模型所起的约束作用应当不同。本文在常规反演的基础上做出了改进,假定阻抗约束系数是一个空间变量,由各地震道的实际地震数据与合成记录之间的振幅残差来确定该道的阻抗约束系数。实际资料应用表明,改进后的反演结果更稳定,能更准确地反映地下阻抗信息。      

浅谈反射地震走时层析中的正则化

反射地震走时层析本质上是一个病态问题,而正则化是改善问题病态程度的有效手段.反射地震走时层析最终可归结为线性方程组的求解,本文讨论了在线性方程组求解过程中正则化的作用和方式.正则化的作用有:(1)用超定分量约束欠定分量和零空间分量;(2)用先验信息约束欠定分量和零空间分量;(3)对射线的不均匀覆盖进行阻尼;(4)对数据的不准确性进行阻尼.正则化的加入方式有:(1)加法型(将正则化矩阵补在层析矩阵后面,包括导数型正则化和零阶正则化,一阶导数型正则化对应最平坦解,二阶导数型正则化对应最光滑解,零阶正则化对应紧约束解);(2)乘法型(将正则化矩阵与层析矩阵相乘,主要包括阻尼型正则化).并利用简单的模型对正则化的效果进行了试验,发现经各种正则化约束后,与未加任何正则化约束得到的速度模型比较,尽管恢复的异常体的幅度不如后者大,但得到的速度剖面要平滑得多,更利于后续的射线追踪正演和层析反演.     

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.     

Adjustment of regularization in ill-posed linear inverse problems by the empirical Bayes approach

Regularization is the most popular technique to overcome the null space of model parameters in geophysical inverse problems, and is implemented by including a constraint term as well as the data‐misfit term in the objective function being minimized. The weighting of the constraint term relative to the data‐fitting term is controlled by a regularization parameter, and its adjustment to obtain the best model has received much attention. The empirical Bayes approach discussed in this paper determines the optimum value of the regularization parameter from a given data set. The regularization term can be regarded as representing a priori information about the model parameters. The empirical Bayes approach and its more practical variant, Akaike's Bayesian Information Criterion, adjust the regularization parameter automatically in response to the level of data noise and to the suitability of the assumed a priori model information for the given data. When the noise level is high, the regularization parameter is made large, which means that the a priori information is emphasized. If the assumed a priori information is not suitable for the given data, the regularization parameter is made small. Both these behaviours are desirable characteristics for the regularized solutions of practical inverse problems. Four simple examples are presented to illustrate these characteristics for an underdetermined problem, a problem adopting an improper prior constraint and a problem having an unknown data variance, all frequently encountered geophysical inverse problems. Numerical experiments using Akaike's Bayesian Information Criterion for synthetic data provide results consistent with these characteristics. In addition, concerning the selection of an appropriate type of a priori model information, a comparison between four types of difference‐operator model – the zeroth‐, first‐, second‐ and third‐order difference‐operator models – suggests that the automatic determination of the optimum regularization parameter becomes more difficult with increasing order of the difference operators. Accordingly, taking the effect of data noise into account, it is better to employ the lower‐order difference‐operator models for inversions of noisy data.     

三维大地电磁自适应L1范数正则化反演

常规三维大地电磁反演的正则项为L₂范数,它以电阻率空间分布函数处处光滑为模型期望,弱化了算法对电性突变界面的分辨能力.本文实现了正则项为L₁范数的三维大地电磁反演算法,让模型空间梯度向量更有机会取得稀疏解,在充分正则的迭代下能够有效突出模型真实电性界面.为避免L₁范数零点不可导带来的求解困难,使用迭代重加权最小二乘法把原问题转换为一系列L₂正则子问题迭代求解.每个子问题的极小方法使用改进型拟牛顿法,其下降方向既能保证正则项海塞矩阵的精确性,又能允许反演过程随迭代灵活更新正则因子.使用比值法或分段衰减法自适应更新正则因子以避免迭代早期陷入奇异解,从而提升反演收敛的稳定性并降低初始模型依赖度.合成的无噪数据反演表明L₁正则算法的模型恢复效果优于L₂正则;不同噪声水平的合成数据反演表明本文的算法具有稳健性;实测数据反演对比表明在合理的正则因子调整策略下,L₁正则反演结果的模型分辨率优于L₂正则.另外,不同初始模型的反演测试还表明,正则因子选取不合理时L₁正则可能造成方块状假异常.     

Inversion-based data-driven time-space domain random noise attenuation method

Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter (PEF), is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data’s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.     

纵向和横向同时约束AVO反演

为了提高AVO(amplitude versus offset)反演结果的精度和横向连续性,本文提出了一种新的AVO反演约束方法,该方法结合贝叶斯原理和卡尔曼滤波算法实现了对反演参数纵向和横向的同时约束.文章首先结合反演参数的纵向贝叶斯先验概率约束和反演参数的横向连续性假设建立了与卡尔曼滤波算法对应的AVO反演系统的数学模型,然后将该数学模型代入卡尔曼滤波算法框架,利用卡尔曼滤波算法实现了双向约束AVO反演.二维模型测试和实际数据测试结果表明,相对于单纯的纵向贝叶斯先验概率约束,双向约束能更准确地刻画参数的横向变化,得到更准确、横向连续性更好的反演结果.     

三维大地电磁自适应正则化有限内存拟牛顿反演

有别于传统基于梯度信息的反演方法在正则化约束中用总梯度逼近海塞逆矩阵的技术,本文将正则化约束问题的数据拟合项和模型光滑项分开考虑,只利用数据拟合函数的梯度信息对数据拟合项的海塞矩阵进行逼近,通过求解类高斯牛顿下降方向方程得到不依赖前几次迭代正则化因子的更精确下降方向,在求解当前迭代下降方向的过程中,通过保证右端项中两个向量的二范数在同一数量级的原则,实现了正则化因子的自动更新.对理论模型的试算表明这种自适应正则化反演方案可以在拟牛顿反演框架下基本达到OCCAM的算法稳定性,反演结果对初始模型依赖性较小,同时又无需在一次迭代中多次搜索最佳正则化因子.本文还基于此算法讨论了大地电磁各参数对于反演结果的影响,由于本文的反演结果能得到充分的正则化约束,因而在此框架下讨论阻抗和倾子在反演中的作用相对更为客观.     

Linearized seismic waveform inversion using a multiple re-weighted least-squares method with QR preconditioning

Linearized inversion methods such as Gauss‐Newton and multiple re‐weighted least‐squares are iterative processes in which an update in the current model is computed as a function of data misfit and the gradient of data with respect to model parameters. The main advantage of those methods is their ability to refine the model parameters although they have a high computational cost for seismic inversion. In the Gauss‐Newton method a system of equations, corresponding to the sensitivity matrix, is solved in the least‐squares sense at each iteration, while in the multiple re‐weighted least‐squares method many systems are solved using the same sensitivity matrix. The sensitivity matrix arising from these methods is usually not sparse, thus limiting the use of standard preconditioners in the solution of the linearized systems. For reduction of the computational cost of the linearized inversion methods, we propose the use of preconditioners based on a partial orthogonalization of the columns of the sensitivity matrix. The new approach collapses a band of co‐diagonals of the normal equations matrix into the main diagonal, being equivalent to computing the least‐squares solution starting from a partial solution of the linear system. The preconditioning is driven by a bandwidth L which can be interpreted as the distance for which the correlation between model parameters is relevant. To illustrate the benefit of the proposed approach to the reduction of the computational cost of the inversion we apply the multiple re‐weighted least‐squares method to the 2D acoustic seismic waveform inversion problem. We verify the reduction in the number of iterations in the conjugate'gradient algorithm as the bandwidth of the preconditioners increases. This effect reduces the total computational cost of inversion as well.     

地层格架正则化约束下的二维立体层析反演

通过把地层格架信息作用于立体层析Fréchet导数矩阵,使得更新后的速度模型呈现出符合地质规律的块状特征.地层格架信息基于立体层析反演中得到的反射点位置进行非规则B样条插值拟合得到,因此在反演中它将会随着反射点位置的更新自然得到更新.与前人提出的保边缘层析算法或多层立体层析算法相比,本文提出的地层格架正则化无需引入混合正则化项或定义某种复杂的混合速度格式,更为直接也更容易实现.理论和实际数据算例证实了该正则化技巧的稳健性和可靠性,能够得到与实际地质构造特征更为一致的地质一致性反演结果.     

二维波动方程速度的正则化-同伦-测井约束反演

针对二维波动方程反问题,将大范围收敛的同伦方法引入速度参数的反演过程中,并将其与求解不适定问题的Tikhonov正则化有机结合,提出了一种新的、特别适用于非线性的、不适定的、多极值的地震勘探反演问题的反演策略：正则化－同伦方法. 为了充分利用测井资料和地震资料的互补特征,进一步提高反演分辨率并压制噪声,设计了正则化-同伦－测井约束联合反演方法. 大量数值试验结果表明了这两种方法的有效性.     

地震与大地电磁测深数据的自适应正则化同步联合反演

在传统的联合反演研究中,地球物理学者往往更多地关注数据拟合,很少涉及正则化理论.本文在电阻率和速度随机分布的大地电磁测深(MT)与地震联合反演研究的基础之上,将正则化思想引入到同步联合反演中,加入先验信息进行模型约束,选取最小模型为稳定泛函,并首次采用自适应正则化算法来确定联合反演的正则化因子.根据以往研究成果,采用非...     

Azimuthally pre-stack seismic inversion for orthorhombic anisotropy driven by rock physics

Based on the long-wavelength approximation, a set of parallel vertical fractures embedded in periodic thin interbeds can be regarded as an equivalent orthorhombic medium. Rock physics is the basis for constructing the relationship between fracture parameters and seismic response. Seismic scattering is an effective way to inverse anisotropic parameters. In this study, we propose a reliable method for predicting the Thomsen’s weak anisotropic parameters and fracture weaknesses in an orthorhombic fractured reservoir using azimuthal pre-stack seismic data. First, considering the influence of fluid substitution in mineral matrix, porosity, fractures and anisotropic rocks, we estimate the orthorhombic anisotropic stiffness coefficients by constructing an equivalent rock physics model for fractured rocks. Further, we predict the logging elastic parameters, Thomsen’s weak parameters, and fracture weaknesses to provide the initial model constraints for the seismic inversion. Then, we derive the P-wave reflection coefficient equation for the inversion of Thomsen’s weak anisotropic parameters and fracture weaknesses. Cauchy-sparse and smoothing-model constraint regularization taken into account in a Bayesian framework, we finally develop a method of amplitude variation with angles of incidence and azimuth (AVAZ) inversion for Thomsen’s weak anisotropic parameters and fracture weaknesses, and the model parameters are estimated by using the nonlinear iteratively reweighted least squares (IRLS) strategy. Both synthetic and real examples show that the method can directly estimate the orthorhombic characteristic parameters from the azimuthally pre-stack seismic data, which provides a reliable seismic inversion method for predicting Thomsen’s weak anisotropic parameters and fracture weaknesses.     

L1 norm inversion method for deconvolution in attenuating media

In order to perform a good pulse compression, the conventional spike deconvolution method requires that the wavelet is stationary. However, this requirement is never reached since the seismic wave always suffers high‐frequency attenuation and dispersion as it propagates in real materials. Due to this issue, the data need to pass through some kind of inverse‐Q filter. Most methods attempt to correct the attenuation effect by applying greater gains for high‐frequency components of the signal. The problem with this procedure is that it generally boosts high‐frequency noise. In order to deal with this problem, we present a new inversion method designed to estimate the reflectivity function in attenuating media. The key feature of the proposed method is the use of the least absolute error (L1 norm) to define both the data and model error in the objective functional. The L1 norm is more immune to noise when compared to the usual L2 one, especially when the data are contaminated by discrepant sample values. It also favours sparse reflectivity when used to define the model error in regularization of the inverse problem and also increases the resolution, since an efficient pulse compression is attained. Tests on synthetic and real data demonstrate the efficacy of the method in raising the resolution of the seismic signal without boosting its noise component.     

Multiscale linearized inversion of surface-wave dispersion curves

In recent years, surface-wave analysis method has been developed rapidly in many fields. Multichannel analysis of surface waves can provide near-surface one-dimensional shear-wave velocity profiles. Because linearized inversion of surface-wave dispersion curves relies heavily on the choice of the initial model, setting an inappropriate initial model can lead to poor inversion results, or even failure of inversion. However, it is difficult to establish a reasonable initial model without a priori information, which is unavailable in most cases. To cope with this problem, a multiscale linearized inversion method is proposed for surface-wave dispersion curves inversion. In contrast with the traditional single-scale linearized inversion, the key idea of the proposed multiscale surface-wave inversion method is the introduction of a merging and splitting process of layers. After every scale inversion, the merging and splitting operations automatically optimize the inversion model, making it gradually approach to a reasonable subsurface stratification. Multiscale surface-wave inversion method reduces the difficulty of establishing the initial model and has high computational efficiency. In addition, it has strong ability to identify high-velocity or low-velocity interlayers and thin layers, especially suited for the geological conditions with obvious stratification. In synthetic tests, the proposed method was compared with the single-scale surface-wave inversion and particle swarm optimization algorithm to demonstrate the effectiveness and practicability of multiscale surface-wave inversion method. We also applied the multiscale surface-wave inversion method to field seismic data acquired in Guizhou, China and Texas, USA. Borehole and crosshole test data were compared with the inversion results of field data to prove the reliability of the proposed method.     

高分辨率Radon变换方法及其在地震信号处理中的应用

Radon变换方法在地震资料处理中广泛采用,在地震同相轴识别和估计方面具有良好效果．无论是倾斜叠加,还是广义Radon变换方法,一般采用最小二乘反演方法实现．目前,在提高反演算法的效率和分辨率方面仍值得研究．本文从倾斜叠加的定义出发,阐明Radon变换分辨率问题的来源和解决办法．采用最小二乘反演方法研究高分辨率抛物线Radon变换和双曲Radon变换时,给出稀疏约束预条件共轭梯度法求解的高分辨率Radon变换的实现方法,同阻尼最小二乘方法相比,分辨率和精度明显提高,文中给出了模型算例．根据有效波和多次波NMO后剩余时差不同,采用高分辨率抛物线和双曲Radon变换可以压制多次波,分别给出了方法原理,最后给出应用实例．研究表明,稀疏约束预条件共轭梯度法可以有效实现高分辨率Radon变换;数值算例表明,算法计算效率和精度较高,可以更好地实现多次波压制．     

基于Shearlet变换和广义全变分正则化的地震数据重建

压缩感知技术通常利用地震信号在某一变换域内的稀疏性质,将随机缺失的地震数据重建问题转化为L1正则化问题.本文首先通过Shearlet变换获得地震信号的稀疏性质,再将广义全变分（TGV）约束引入L1正则化模型,构建了基于Shearlet变换的双正则化模型用于重建地下介质的图像.与传统L1正则化方法相比,基于Shearlet变换的双正则化方法不仅考虑了信号的稀疏性,同时兼顾了地下介质结构的复杂性,可以较好的重建地下结构体的图像.最后采用交替方向乘子法（ADMM）求解所建模型,每个子问题均可得到显式解.数值实验对比了基于小波变换、Shearlet变换的L1正则化方法和TGV正则化方法,结果表明基于Shearlet变换的双正则化方法对于随机采样50%数据的情况具有较好的重建结果,同时对于有限范围的连续缺失数据的重建亦具有一定的有效性.     

Double parameterized regularization inversion method for migration velocity analysis in transversely isotropic media with a vertical symmetry axis

Simultaneous estimation of velocity gradients and anisotropic parameters from seismic reflection data is one of the main challenges in transversely isotropic media with a vertical symmetry axis migration velocity analysis. In migration velocity analysis, we usually construct the objective function using the l₂ norm along with a linear conjugate gradient scheme to solve the inversion problem. Nevertheless, for seismic data this inversion scheme is not stable and may not converge in finite time. In order to ensure the uniform convergence of parameter inversion and improve the efficiency of migration velocity analysis, this paper develops a double parameterized regularization model and gives the corresponding algorithms. The model is based on the combination of the l₂ norm and the non‐smooth l₁ norm. For solving such an inversion problem, the quasi‐Newton method is utilized to make the iterative process stable, which can ensure the positive definiteness of the Hessian matrix. Numerical simulation indicates that this method allows fast convergence to the true model and simultaneously generates inversion results with a higher accuracy. Therefore, our proposed method is very promising for practical migration velocity analysis in anisotropic media.