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1.
AVAZ方法是裂缝储层评价的重要手段,然而它受到静态等效地震岩石物理模型的限制,一般用于反演裂缝密度和裂缝走向.近年来,随着裂缝性孔隙介质等效模型的发展,频变AVAZ响应特征被证明可以携带更多的裂缝性质信息.本文针对HTI介质,基于Chapman理论分析流体类型和裂缝长度对频变AVAZ响应特征的影响并建立结合贝叶斯理论和遗传算法反演裂缝密度、裂缝走向、裂缝长度和裂缝充填流体类型的新方法.新反演方法流程为:首先,基于频变AVAZ特征建立反演目标函数并运用遗传算法求解,根据此结果分别建立裂缝参数的似然函数;其次,利用贝叶斯算法将地质、测井等获得的裂缝性质先验信息与地震获得的裂缝性质似然函数结合,计算裂缝性质的后验概率分布函数;最后,通过控制先验裂缝参数的方差,均衡地震数据及地质、测井数据的信息采用量.合成数据的分析和反演结果表明:流体性质和裂缝长度是频变AVAZ响应的敏感参数;本文提出反演裂缝性质方法是可行的.先验裂缝参数影响分析验证了该方法在均衡地质、测井及地震数据的信息采用量方面的有效性.  相似文献   

2.
常规AVO三参数反演是通过Zoeppritz方程的近似公式来建立AVO正演模拟的过程,然而在P波入射角过临界角和弹性参数在纵向上变化剧烈的情况下,Zoeppritz方程近似公式精度有限.针对这种情况,可以使用精确的Zoeppritz方程来构建反演目标函数,由于精确Zoeppritz方程中P波反射系数和弹性参数之间是一种复杂的非线性关系,通常解决途径是利用非线性的优化算法来进行数值计算,但是非线性优化算法的缺点是计算量过大;另外一种途径是利用广义线性反演的方法,通过泰勒一阶展开式将P波反射振幅展开后,用线性关系近似表达非线性关系,经过几次迭代后,在理论上可以达到很高的精度,但是广义线性反演算法的核心部分--Jacobian矩阵由于矩阵条件数过大,往往会造成反演算法的不稳定,其应用范围得到了限制.贝叶斯反演方法是通过引入模型参数的先验分布结合噪声的似然函数,生成模型参数的后验分布,通过求取模型参数的最大后验概率分布来得到模型参数的反演解,由于引入模型参数的先验分布信息,可以有效的降低反演的不适定问题.本文将两种反演算法的思想相结合,利用广义线性反演算法的思想,构建AVO正演模拟的过程来提高大角度地震数据反演的精度,同时结合贝叶斯理论,通过引入模型参数的先验分布信息构建反演目标函数的正则化项,可以有效降低由于Jacob矩阵条件数过大带来的反演不适定问题,该算法假设模型参数服从三变量柯西分布.  相似文献   

3.
多尺度地震资料联合反演方法研究   总被引:9,自引:3,他引:6       下载免费PDF全文
常规三维地面地震反演不可避免的存在多解性和分辨率不高的缺陷,而油藏地球物理阶段丰富的多尺度地震资料为减小多解性、提高分辨率提供了可能.基于贝叶斯反演理论,通过联合概率分布建立新的似然函数,将三维地面地震、VSP和井间地震三种多尺度资料有机地融合在一起,完善了多尺度地震资料联合反演框架及反演流程.模型测试及实际资料处理表明,联合反演算法有效地引入了小尺度地震资料中的高频信息对大尺度资料进行约束,反演结果在保留大尺度地震资料特征的基础上提高了分辨率,降低了多解性,同时促进了多种地震资料之间的相互匹配.  相似文献   

4.
频率域航空电磁数据变维数贝叶斯反演研究   总被引:5,自引:2,他引:3       下载免费PDF全文
传统的梯度反演方法已经广泛应用于频率域航空电磁数据处理中,然而此类方法受初始模型影响较大,且容易陷入局部极小.为解决这一问题,本文采用改进的变维数贝叶斯反演方法实现航空电磁数据反演.该方法根据建议分布对反演模型进行随机采样,并依据接受概率筛选合理的候选模型,最终获得反演模型的概率分布和不确定度信息.为解决贝叶斯反演方法对深部低阻层反演效果不佳的问题,本文通过引入合理加权系数,调整对反演模型约束强度,在很大程度上改善了反演效果.通过对模型统计方法进行改进,在遵循原有模型采样方法和接受标准的基础上,将满足数据拟合要求的模型纳入统计范围,削弱不合理模型对统计结果的干扰.本文最后通过对含有高斯噪声的理论数据和实测数据进行反演,并与Occam反演结果进行对比,验证了该方法的有效性.  相似文献   

5.
提出了各向异性页岩储层统计岩石物理反演方法.通过统计岩石物理模型建立储层物性参数与弹性参数的定量关系,使用测井数据及井中岩石物理反演结果作为先验信息,将地震阻抗数据定量解释为储层物性参数、各向异性参数的空间分布.反演过程在贝叶斯框架下求得储层参数的后验概率密度函数,并从中得到参数的最优估计值及其不确定性的定量描述.在此过程中综合考虑了岩石物理模型对复杂地下介质的描述偏差和地震数据中噪声对反演不确定性的影响.在求取最大后验概率过程中使用模拟退火优化粒子群算法以提高收敛速度和计算准确性.将统计岩石物理技术应用于龙马溪组页岩气储层,得到储层泥质含量、压实指数、孔隙度、裂缝密度等物性,以及各向异性参数的空间分布及相应的不确定性估计,为页岩气储层的定量描述提供依据.  相似文献   

6.
本文研究了一种基于随机地震反演的Russell流体因子直接估算方法,该方法是一种基于蒙特卡罗的非线性反演,能够有效地融合测井资料中的高频信息,提高反演结果的分辨率.本文应用贝叶斯理论框架,首先通过测井数据计算井位置处的Russell流体因子,利用序贯高斯模拟方法(sequential Gaussian simulation,SGS)得到流体因子的先验信息;然后构建似然函数;最后利用Metropolis抽样算法对后验概率密度进行抽样,得到反演的Russell流体因子.其中对每道数据进行序贯高斯模拟时,采用一种新的逐点模拟方式,具有较高的计算速度.数值试验表明:反演结果与理论模型和实际测井数据吻合较好,具有较高的分辨率,对于判识储层含流体特征具有较好的指示作用.  相似文献   

7.
瑞利波具有强能量和频散特性,面波勘探采集瑞利面波数据分析反演得到横波速度结构,在浅地表勘探领域得到了广泛的应用,根据面波频散反演速度结构是面波勘探的重要环节之一.面波频散与地下介质的弹性参数是非线性关系,全局优化方法是解决非线性反问题的有效办法,贝叶斯方法是一种基于统计的全局优化方法.贝叶斯公式反映了先验信息和条件概率乘积与后验概率之间的正比关系,利用马尔科夫链蒙特卡洛采样方法可以获得后验分布的采样.与其他方法相比,该方法不是给出一个模型最优解,而是统计出模型参数的平均值和方差,平均值即是反演的模型解,方差能对该反演结果的不确定性做出评价.本文采用贝叶斯方法对模拟和实测瑞利面波数据频散曲线进行反演,结果显示,该方法能获得比较精确的横波速度和厚度参数,验证了本文方法可行.在高丽营地区采集了多个单炮面波数据,对数据进行频散谱处理拾取频散曲线,通过贝叶斯反演方法获得了该测线二维横波速度剖面图,有助于划分和解释该测区地层及断层地质构造.  相似文献   

8.
本文提出的储层物性参数同步反演是一种高分辨率的非线性反演方法,该方法综合利用岩石物理和地质统计先验信息,在贝叶斯理论框架下,首先通过变差结构分析得到合理的变差函数,进而利用快速傅里叶滑动平均模拟算法(Fast Fourier TransformMoving Average,FFT-MA)和逐渐变形算法(Gradual Deformation Method,GDM)得到基于地质统计学的储层物性参数先验信息,然后根据统计岩石物理模型建立弹性参数与储层物性参数之间的关系,构建似然函数,最终利用Metropolis算法实现后验概率密度的抽样,得到物性参数反演结果。并将此方法处理了中国陆上探区的一块实际资料,本方法的反演结果具有较高的分辨率,与测井数据吻合度较高;由于可以直接反演储层物性参数,避免了误差的累积,大大减少了不确定性的传递,且计算效率较高。  相似文献   

9.
基于地质统计先验信息的储层物性参数同步反演   总被引:4,自引:1,他引:3  
本文提出的储层物性参数同步反演是一种高分辨率的非线性反演方法,该方法综合利用岩石物理和地质统计先验信息,在贝叶斯理论框架下,首先通过变差结构分析得到合理的变差函数,进而利用快速傅里叶滑动平均模拟算法(Fast Fourier TransformMoving Average,FFT-MA)和逐渐变形算法(Gradual Deformation Method,GDM)得到基于地质统计学的储层物性参数先验信息,然后根据统计岩石物理模型建立弹性参数与储层物性参数之间的关系,构建似然函数,最终利用Metropolis算法实现后验概率密度的抽样,得到物性参数反演结果。并将此方法处理了中国陆上探区的一块实际资料,本方法的反演结果具有较高的分辨率,与测井数据吻合度较高;由于可以直接反演储层物性参数,避免了误差的累积,大大减少了不确定性的传递,且计算效率较高。  相似文献   

10.
基于贝叶斯理论的AVO三参数波形反演   总被引:24,自引:7,他引:24       下载免费PDF全文
在实际的AVO反演问题中,叠前数据体中的噪声或其他因素严重影响了AVO反演问题的适定性,而采用先验地质信息作为AVO反演问题的约束条件是解决AVO反演问题不适定的一种可行方法. 文中的似然函数采用了[WTBX]ι[WTBX]p范数的解,并用Cauchy分布表示先验模型参数的分布. 以此为基础,在反演中建立了测井数据的参数协方差矩阵对反演过程进行约束,并采用了共轭梯度算法实现多参数非线性的反演过程. 同时,为了提高反演精度,避免动校正拉伸及依赖于炮检距的调谐效应对参数估计的影响,反演采用动校前地震数据进行参数估计. 从应用效果分析来看,即使叠前道集的信噪比不高,反演的结果也能较好地与实际情况相匹配,为识别储层流体性质提供了新的手段.  相似文献   

11.
Probability theory as logic (or Bayesian probability theory) is a rational inferential methodology that provides a natural and logically consistent framework for source reconstruction. This methodology fully utilizes the information provided by a limited number of noisy concentration data obtained from a network of sensors and combines it in a consistent manner with the available prior knowledge (mathematical representation of relevant physical laws), hence providing a rigorous basis for the assimilation of this data into models of atmospheric dispersion for the purpose of contaminant source reconstruction. This paper addresses the application of this framework to the reconstruction of contaminant source distributions consisting of an unknown number of localized sources, using concentration measurements obtained from a sensor array. To this purpose, Bayesian probability theory is used to formulate the full joint posterior probability density function for the parameters of the unknown source distribution. A simulated annealing algorithm, applied in conjunction with a reversible-jump Markov chain Monte Carlo technique, is used to draw random samples of source distribution models from the posterior probability density function. The methodology is validated against a real (full-scale) atmospheric dispersion experiment involving a multiple point source release.  相似文献   

12.
A probabilistic approach to structural model updating   总被引:3,自引:0,他引:3  
The problem of updating a structural model and its associated uncertainties by utilizing measured dynamic response data is addressed. A Bayesian probabilistic formulation is followed to obtain the posterior probability density function (PDF) of the uncertain model parameters for given measured data. The present paper discusses the issue of identifiability of the model parameters and reviews existing asymptotic approximations for identifiable cases. The focus of the paper is on the treatment of the general unidentifiable case where the earlier approximations are not applicable. In this case the posterior PDF of the parameters is found to be concentrated in the neighborhood of an extended and extremely complex manifold in the parameter space. The computational difficulties associated with calculating the posterior PDF in such cases are discussed and an algorithm for an efficient approximate representation of the above manifold and the posterior PDF is presented. Numerical examples involving noisy data are presented to demonstrate the concepts and the proposed method.  相似文献   

13.
三维重力反演是地质工作者了解地球深部构造,认知地下结构的重要手段.按照反演单元划分,三维重力反演有离散多面体(Discrete)反演和网格节点(Voxels)反演两种方式.离散多面体反演由于易于吸收先验地质信息得到的理论场能够很好地拟合观测场,因此,在实际重力反演中更受欢迎.目前离散多面体重力反演中初始模型的建立方法繁杂不一,实际应用受到很大的限制.本文本着充分挖掘利用先验信息和重力观测数据得到丰富可靠的反演结果这一原则,以离散多面体反演技术为基础,改进建模过程.在初始模型的建立中,吸收贝叶斯算法优势,采用隐马尔科夫链改善朴素贝叶斯方法的分类效果,通过最大似然函数算法求解,再采取模型降阶技术,固定所建模型中几何体的形态或密度,达到在几何体形态(x,y,z)、密度(σ)和重力值(g)五个参数中降低维数目的,从而减小高维不确定性和正演的计算量,由此反演计算的地质体密度和分布范围相对更准确,更利于重现重力模型结构.通过单位球体和任意形态几何体模拟实验,以及安徽省泥河矿区三维重力反演实践,得到非常接近实际的密度或重力值,大幅提高了三维重力反演的精度和效率,说明该方法是有效、实用的.  相似文献   

14.
Amplitude variations with offset or incident angle (AVO/AVA) inversion are typically combined with statistical methods, such as Bayesian inference or deterministic inversion. We propose a joint elastic inversion method in the time and frequency domain based on Bayesian inversion theory to improve the resolution of the estimated P- and S-wave velocities and density. We initially construct the objective function using Bayesian inference by combining seismic data in the time and frequency domain. We use Cauchy and Gaussian probability distribution density functions to obtain the prior information for the model parameters and the likelihood function, respectively. We estimate the elastic parameters by solving the initial objective function with added model constraints to improve the inversion robustness. The results of the synthetic data suggest that the frequency spectra of the estimated parameters are wider than those obtained with conventional elastic inversion in the time domain. In addition, the proposed inversion approach offers stronger antinoising compared to the inversion approach in the frequency domain. Furthermore, results from synthetic examples with added Gaussian noise demonstrate the robustness of the proposed approach. From the real data, we infer that more model parameter details can be reproduced with the proposed joint elastic inversion.  相似文献   

15.
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.  相似文献   

16.
Electrical resistivity tomography is a non-linear and ill-posed geophysical inverse problem that is usually solved through gradient-descent methods. This strategy is computationally fast and easy to implement but impedes accurate uncertainty appraisals. We present a probabilistic approach to two-dimensional electrical resistivity tomography in which a Markov chain Monte Carlo algorithm is used to numerically evaluate the posterior probability density function that fully quantifies the uncertainty affecting the recovered solution. The main drawback of Markov chain Monte Carlo approaches is related to the considerable number of sampled models needed to achieve accurate posterior assessments in high-dimensional parameter spaces. Therefore, to reduce the computational burden of the inversion process, we employ the differential evolution Markov chain, a hybrid method between non-linear optimization and Markov chain Monte Carlo sampling, which exploits multiple and interactive chains to speed up the probabilistic sampling. Moreover, the discrete cosine transform reparameterization is employed to reduce the dimensionality of the parameter space removing the high-frequency components of the resistivity model which are not sensitive to data. In this framework, the unknown parameters become the series of coefficients associated with the retained discrete cosine transform basis functions. First, synthetic data inversions are used to validate the proposed method and to demonstrate the benefits provided by the discrete cosine transform compression. To this end, we compare the outcomes of the implemented approach with those provided by a differential evolution Markov chain algorithm running in the full, un-reduced model space. Then, we apply the method to invert field data acquired along a river embankment. The results yielded by the implemented approach are also benchmarked against a standard local inversion algorithm. The proposed Bayesian inversion provides posterior mean models in agreement with the predictions achieved by the gradient-based inversion, but it also provides model uncertainties, which can be used for penetration depth and resolution limit identification.  相似文献   

17.
岩相信息能够反映储层岩性及流体特征,在地震储层预测中具有重要作用.常规方法主要利用与岩相信息关系密切的弹性参数定性或定量地转化为岩相信息.在实际应用中,弹性参数的获取主要基于叠前地震反演技术.而不同弹性参数的叠前地震反演精度间存在着差异,势必影响岩相的整体预测精度.本文提出对弹性参数进行加权统计来预测岩相.首先,基于贝叶斯理论,引入权重系数来调节弹性参数信息的采用量,构建出最终的目标反演函数;其次,考虑到勘探初期缺少明确的测井岩相信息,提出利用高斯混合分布函数来自动估算岩相先验概率;最后,根据输入弹性参数的取值,计算每类岩相对应的后验概率密度,将目标反演函数取最大后验概率密度时对应的岩相类别作为最终预测的岩相.新方法旨在减少弹性参数精度间的精度差异对岩相预测结果的影响,以期提高地震岩相的预测精度.模型与实际资料测试均表明该方法可行、有效且预测精度较高.  相似文献   

18.
The similarity between maximum entropy (MaxEnt) and minimum relative entropy (MRE) allows recent advances in probabilistic inversion to obviate some of the shortcomings in the former method. The purpose of this paper is to review and extend the theory and practice of minimum relative entropy. In this regard, we illustrate important philosophies on inversion and the similarly and differences between maximum entropy, minimum relative entropy, classical smallest model (SVD) and Bayesian solutions for inverse problems. MaxEnt is applicable when we are determining a function that can be regarded as a probability distribution. The approach can be extended to the case of the general linear problem and is interpreted as the model which fits all the constraints and is the one model which has the greatest multiplicity or “spreadout” that can be realized in the greatest number of ways. The MRE solution to the inverse problem differs from the maximum entropy viewpoint as noted above. The relative entropy formulation provides the advantage of allowing for non-positive models, a prior bias in the estimated pdf and `hard' bounds if desired. We outline how MRE can be used as a measure of resolution in linear inversion and show that MRE provides us with a method to explore the limits of model space. The Bayesian methodology readily lends itself to the problem of updating prior probabilities based on uncertain field measurements, and whose truth follows from the theorems of total and compound probabilities. In the Bayesian approach information is complete and Bayes' theorem gives a unique posterior pdf. In comparing the results of the classical, MaxEnt, MRE and Bayesian approaches we notice that the approaches produce different results. In␣comparing MaxEnt with MRE for Jayne's die problem we see excellent comparisons between the results. We compare MaxEnt, smallest model and MRE approaches for the density distribution of an equivalent spherically-symmetric earth and for the contaminant plume-source problem. Theoretical comparisons between MRE and Bayesian solutions for the case of the linear model and Gaussian priors may show different results. The Bayesian expected-value solution approaches that of MRE and that of the smallest model as the prior distribution becomes uniform, but the Bayesian maximum aposteriori (MAP) solution may not exist for an underdetermined case with a uniform prior.  相似文献   

19.
The similarity between maximum entropy (MaxEnt) and minimum relative entropy (MRE) allows recent advances in probabilistic inversion to obviate some of the shortcomings in the former method. The purpose of this paper is to review and extend the theory and practice of minimum relative entropy. In this regard, we illustrate important philosophies on inversion and the similarly and differences between maximum entropy, minimum relative entropy, classical smallest model (SVD) and Bayesian solutions for inverse problems. MaxEnt is applicable when we are determining a function that can be regarded as a probability distribution. The approach can be extended to the case of the general linear problem and is interpreted as the model which fits all the constraints and is the one model which has the greatest multiplicity or “spreadout” that can be realized in the greatest number of ways. The MRE solution to the inverse problem differs from the maximum entropy viewpoint as noted above. The relative entropy formulation provides the advantage of allowing for non-positive models, a prior bias in the estimated pdf and `hard' bounds if desired. We outline how MRE can be used as a measure of resolution in linear inversion and show that MRE provides us with a method to explore the limits of model space. The Bayesian methodology readily lends itself to the problem of updating prior probabilities based on uncertain field measurements, and whose truth follows from the theorems of total and compound probabilities. In the Bayesian approach information is complete and Bayes' theorem gives a unique posterior pdf. In comparing the results of the classical, MaxEnt, MRE and Bayesian approaches we notice that the approaches produce different results. In␣comparing MaxEnt with MRE for Jayne's die problem we see excellent comparisons between the results. We compare MaxEnt, smallest model and MRE approaches for the density distribution of an equivalent spherically-symmetric earth and for the contaminant plume-source problem. Theoretical comparisons between MRE and Bayesian solutions for the case of the linear model and Gaussian priors may show different results. The Bayesian expected-value solution approaches that of MRE and that of the smallest model as the prior distribution becomes uniform, but the Bayesian maximum aposteriori (MAP) solution may not exist for an underdetermined case with a uniform prior.  相似文献   

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