用等厚薄层权重自适应迭代阻尼最小二乘法反演瑞雷波频散曲线

笔者在前人工作基础上提出的等厚薄层权重自适应迭代阻尼最小二乘法,能有效地解除对初始模型的依赖.理论模型与实际资料试算结果表明,用该算法对瑞雷波频散曲线进行反演,具有稳定性好、精度高、分辨能力强的特点,并能自动分层和反演地层参数.     

Geostatistical Seismic Inversion with Direct Sequential Simulation and Co-simulation with Multi-local Distribution Functions

Stochastic sequential simulation is a common modelling technique used in Earth sciences and an integral part of iterative geostatistical seismic inversion methodologies. Traditional stochastic sequential simulation techniques based on bi-point statistics assume, for the entire study area, stationarity of the spatial continuity pattern and a single probability distribution function, as revealed by a single variogram model and inferred from the available experimental data, respectively. In this paper, the traditional direct sequential simulation algorithm is extended to handle non-stationary natural phenomena. The proposed stochastic sequential simulation algorithm can take into consideration multiple regionalized spatial continuity patterns and probability distribution functions, depending on the spatial location of the grid node to be simulated. This work shows the application and discusses the benefits of the proposed stochastic sequential simulation as part of an iterative geostatistical seismic inversion methodology in two distinct geological environments in which non-stationarity behaviour can be assessed by the simultaneous interpretation of the available well-log and seismic reflection data. The results show that the elastic models generated by the proposed stochastic sequential simulation are able to reproduce simultaneously the regional and global variogram models and target distribution functions relative to the average volume of each sub-region. When used as part of a geostatistical seismic inversion procedure, the retrieved inverse models are more geologically realistic, since they incorporate the knowledge of the subsurface geology as provided, for example, by seismic and well-log data interpretation.     

Series Expansion-Based Genetic Inversion of Wireline Logging Data

An evolutionary approach is applied to solve the nonlinear well logging inverse problem. In the framework of the proposed interval inversion method, nuclear, sonic, and laterolog resistivity data measured at an arbitrary depth interval are jointly inverted, where the depth variation of porosity, water saturation, and shale volume is expanded into series using Legendre polynomials as basis functions. In the interval inversion procedure, the series expansion coefficients are estimated by using an adaptive float-encoded genetic algorithm. Since the solution of the inverse problem using traditional linear optimization tools highly depends on the selection of the initial model, a heuristic search is necessary to reduce the initial model dependence of the interval inversion procedure. The genetic inversion strategy used in interval inversion seeks the global extreme of the objective function and provides an estimate of the vertical distribution of petrophysical parameters, even starting the inversion procedure from extremely high distances from the optimum. For a faster computational process, after a couple of thousand generations, the genetic algorithm is replaced by some linear optimization steps. The added advantage of using the Marquardt algorithm is the possibility to characterize the accuracy of the series expansion coefficients and derived petrophysical properties. A Hungarian oil field example demonstrates the feasibility and stability of the improved interval inversion method. As a significance, the genetic inversion method does not require prior knowledge or strong restrictions on the values of petrophysical properties and gives highly reliable estimation results practically independent of the initial model and core information.

     

Inverse analysis for geomaterial parameter identification using Pareto multiobjective optimization

An inverse analysis method that combines the back propagation neural network (BPNN) and vector evaluated genetic algorithm (VEGA) was proposed to identify mechanical geomaterial parameters for a more accurate prediction of deformation. The BPNN is used to replace the time‐consuming numerical calculations, thus enhancing the efficiency of the inverse analysis. The VEGA is used to find the Pareto‐optimal solutions to multiobjective functions. Unlike traditional back‐analysis methods which are based on only 1 type of field measurement and a single objective function, this proposed method can consider multiple field observations simultaneously. The proposed method was applied to the Shapingba foundation pit excavation located in Chongqing city, China. Two types of measurements are considered in the method simultaneously: the displacements in the x‐direction (north orientation) and those in the y‐direction (east orientation). Five deformation modulus parameters for artificial backfill soil, silty clay, siltstone, sandstone, and mudstone were selected as the inversion parameters. Compared with the weighted sum approach, the proposed method was demonstrated as an efficient multi‐objective optimization tool for back calculating undetermined parameters. After performing a forward‐calculation using the optimized parameters obtained by the inverse analysis, the predicted results were well consistent with the practical deformation in magnitude and trend.     

改进的贝叶斯迭代反演方法及其在白云岩致密储层识别的应用

由于低孔隙度和低渗透率的白云岩致密油储集层的纵波阻抗与其围岩差异非常小,利用叠后反演技术难以有效预测储层,而可以提取丰富弹性信息的叠前反演是解决此问题的有效手段,但是由于噪声等问题,叠前反演方程有较强的不适定性,笔者在贝叶斯框架下引入了改进的多变量柯西分布和改进的低频约束因子,重新推导了反演方程,获得了新的目标函数,有效地减少了反演的不适定性,从而提高了反演的稳定性,并结合迭代的思想来不断更新反演求解过程中的背景纵横波速度比值,从而增加了反演结果的精度。模型数据测试和实际资料应用都证明了该方法的稳定性和适用性。统计表明,利用提出的反演方法,目的层段内优质储层厚度预测吻合率高达89.75%。因此,此方法对类似硅质致密储层的勘探有重要的借鉴意义。     

S-wave velocity profiling from refraction microtremor Rayleigh wave dispersion curves via PSO inversion algorithm

The refraction microtremor method has been increasingly used as an appealing tool for investigating near surface S-wave structure. However, inversion, as a main stage in processing refraction microtremor data, is challenging for most local search methods due to its high nonlinearity. With the development of data optimization approaches, fast and easier techniques can be employed for processing geophysical data. Recently, particle swarm optimization algorithm has been used in many fields of studies. Use of particle swarm optimization in geophysical inverse problems is a relatively recent development which offers many advantages in dealing with the nonlinearity inherent in such applications. In this study, the reliability and efficiency of particle swarm optimization algorithm in the inversion of refraction microtremor data were investigated. A new framework was also proposed for the inversion of refraction microtremor Rayleigh wave dispersion curves. First, particle swarm optimization code in MATLAB was developed; then, in order to evaluate the efficiency and stability of proposed algorithm, two noise-free and two noise-corrupted synthetic datasets were inverted. Finally, particle swarm optimization inversion algorithm in refraction microtremor data was applied for geotechnical assessment in a case study in the area in city of Tabriz in northwest of Iran. The S-wave structure in the study area successfully delineated. Then, for evaluation, the estimated Vs profile was compared with downhole data available around of the considered area. It could be concluded that particle swarm optimization inversion algorithm is a suitable technique for inverting microtremor waves.     

关于频率电磁测深几个问题的探讨(七)——解析广义逆矩阵反演法

从目标函数的线性化出发,采用奇异值分解法求广义逆矩阵并适当加阻尼,用迭代法直接解超定方程实现改进广义逆矩阵反演。进一步从理论与实际结合上阐明改进广义逆矩阵反演法的灵活、稳定,它涵盖了多种反演方法的优点,又具有自身特点,在实际应中适应性强,并能提供一些辅助信息,可更好地评价解释结果。      

混沌控制反演系统构造及算法逻辑设计

给出了适用于非线性反演的混沌控制反演系统构造方法及求解控制矩阵的算法逻辑设计。该方法在迭代反演控制参数和迭代反演输出结果之间建立耦合关系，通过时时修改控制参数保证迭代稳定收敛到预期的解空间。在求解过程中，应用数据结构方法分析了混沌控制理论中计算控制矩阵的数据结构，采用树形结构表述出控制矩阵计算过程中下标的取值逻辑，利用可以复读栈中元素的中序遍历来遍历每一棵树，寻找各元素的组合序列，给出的数值算例说明了混沌控制反演方法及其算法的有效性。     

The Rapid Inversion of 3-D Potential Field and Program Design

The application of three-dimensional inversion of gravity and magnetic fields is very important not only in geophysical researches, but also in the study of geological structures. A formula of potential field in frequency-domain, developed by Parker in 1973, can be used as a rapid and effective algorithm in gravity and magnetic inversion. The technique has been improved then by Oldenburg, Sprenke, Feng and others.In addition to a brief introduction of Parker's algorithm and its applications, this paper includes the following five parts: basic computational techniques, inversion of single layer, convergence and constraints, simultaneous inversion for density and topography as well as inversion of multilayers. The authors present relevant practical iterative formulas and its varieties when density distribution varies with depth in linear or exponential relation. In order to maintain computation stability and speed up iteration convergence, some approaches are taken in the program design, for instance shifting lower interface of the studied layer, inverting corrections of topography, reducing grid boundary effects and utilizing low-pass filter. With the consideration of the nonuniqueness of the inversion, a method of using seismic data to constrain the range ofpossible models is discussed. It is pointed out that the density variation generates less effects than those of topography on the spectrum of gravity anomaly in second order. Therefore density contrast and topography can be inverted simultaneously by an alternative weighting iteration. By analogy, the inversion of multilayer model can be done in the above procedure. An approach of model decomposition is useful in the computation of multilayer model. The techniques discussed in the present paper for gravitational field are also valid for magnetic field.     

基于遗传算法的岩土力学参数反演及其在 ABAQUS中的实现

岩土工程优化反分析是一个典型的复杂非线性函数优化问题,采用全局优化算法是解决这个问题的理想途径。结合ABAQUS有限元软件,提出遗传算法与有限元联合反演法,将有限元程序作为一个单独模块嵌入到遗传算法程序中,以测点的实测值与计算值建立误差函数,编制了遗传算法反演分析程序。并给出应用实例验证了该法的有效性,表明该方法可应用于岩土工程中的反演分析工作。     

On the application of combined geoelectric weighted inversion in environmental exploration

Geophysical surveying methods are of great importance in environmental exploration. Inversion-based data processing methods are applied for the determination of geometrical and physical parameters of the target model. The use of this geoelectric inversion method is advantageous in environmental research where highly reliable information with large spatial resolution is required. The 2D combined geoelectric inversion (CGI) method performs more accurate parameter estimation than conventional 1D single inversion methods by efficiently decreasing the number of unknowns of the inverse problem (single means that data sets of individual vertical electric sounding stations are inverted separately). The quality improvement in parameter space is demonstrated by comparing the traditional 1D inversion procedure with a 2D series expansion-based inversion technique. The CGI method was further developed by weighting individual direct current geoelectric data sets automatically in order to improve inversion results. The new algorithm was named combined geoelectric weighted inversion, which extracts the solution by a special weighted least squares technique. It is shown that the new inversion methodology is applicable to resolve near-surface structures such as rapidly varying layer boundaries, laterally inhomogeneous formations and pinch-outs.     

基于重力全张量数据的Parker-Oldenburg算法研究

相比重力数据,重力张量数据通常包含更多的异常信息。本文根据重力数据与重力张量数据的关系,利用位场转化技术,将重力张量数据应用于传统的Parker-Oldenburg密度界面反演算法中。通过模型试验,证明了在网格间距较大或者数据存在一定噪音时,使用本文算法进行反演能得到更好的效果。实验结果说明利用重力张量数据可以有效地提高密度界面反演的分辨率。     

改进型粒子群算法及其在GPR全波形反演中的应用

探地雷达作为高精度的物探工作方法,其主要目的是反演解释地下结构的物性参数。笔者提出社会学习型粒子群优化反演方法,它以信号均方误差为目标函数,用时域有限差分方法作正演,并且针对反射波信号较弱、反演效果不佳的情况设计了对正演结果进行振幅补偿的方法,对反射波的振幅进行增益,以提高反演精度。通过与经典粒子群优化反演方法的结果对比,说明了该算法在准确度以及效率方面都有相当大的提高。经过分析多层介质仿真数据的一维反演结果,说明了该算法对多参数反演的有效性和良好的抗噪性。     

Stable methods of interpretation of gravimetric data

A method for interpretation of potential geophysical fields, based on a stable inversion algorithm, is proposed. The stability of the algorithm is provided by an original choice of the zero approximation model and stepwise solution of the inverse problem by a correctness set. The three-dimensional density distribution of local structures as grid functions is reconstructed by layer-wise anomalies of a spilt field. Examples of interpretation of the practical gravimetric data illustrating the efficiency of the method are given.

     

三维电阻率成像新方法及应用

三维电阻率成像法包括近似三维反演因子和综合子空间解释器,三维反演因子用于正向线性电阻率近似解译,综合子空间法是共轭梯度法的变换,是求解大型最优化问题的有效方法.三维成像是线性反演问题,不需正演模拟或敏感度修改.在非线性三维反演中,三维电阻率成像技术可直接获取三维电阻率的分布信息,或提供中间过程的模式修改.野外资料验证表明,三维成像技术可提供电阻率空间的分布信息.     

频率域反射波全波形速度反演

全波形反演不仅利用相位和振幅信息,还利用波形的细节变化,具有刻画模型精确细节的能力.在对稀疏矩阵直接LU分解求解的基础上,采用梯度预处理方法对声波介质速度模型进行了反射波全波形反演.采用误差反向传播算法计算目标函数梯度以及伪Hessian矩阵的对角线元素来做梯度预处理.数值模型的实验结果表明,利用有效的频率段便能反演出分辨率较高的速度结构,用低频反演出的结果作为高频反演的初始模型,减少了解的非唯一性.二维高斯光滑初始模型提供了有利的低频信息,得到较好的反演结果.伪Hessian矩阵的预处理吸收了高斯牛顿法的二次收敛优势,在不增加计算量的前提下,加快收敛速度.      

波阻抗约束反演的几种方法

介绍三种理论与实际效果比较好的波阻抗反演方法,即宽带约束反演(BCI)、快速模拟退火算法(FSA)和正则化方法。BCI是一种线性迭代方法,FSA是一种具有全局寻优特点的随机搜索方法,正则化方法是通过对目标函数施加先验约束,来解决反演的病态问题———多解性和不稳定性。最后,使用正则化方法并结合快速模拟退火算法,进行了理论模型试算和实际资料处理,实用效果比较好。     

瞬态瑞雷波反演横波的SVD算法及其应用

利用奇异值分解算法对瑞雷波频散曲线进行反演,提高横波速度的反演精度,引入权重矩阵提高数据分辨率,采用自适应修改阻尼因子提高迭代效果并协调分辨率与解的关系.某高速公路软土路基实测资料试算结果表明,用该算法对瞬态瑞雷波频散曲线进行反演,不但具有稳定性好、精度高、分辨能力强的特点,而且能自动分层和反演地层参数,在高速公路软土路基探测等领域中具有广阔的应用前景.     

一种大坝渗透系数分区反演新方法研究

基于人工神经网络的非线性映射特征,在渗流有限元计算的基础上,结合水头和渗流量等实测资料提出了大坝渗透系数的反演方法。为了克服经典神经网络存在的缺陷,提出了模拟退火的交替迭代算法神经网络新方法。在相同的初始条件下,用该新方法和经典网络进行了比较,得出前者的优越性和有效性。同时将该方法用于大坝的渗流反分析,利用反演出的渗透系数进行渗流场计算。数值计算结果表明,这种方法对大坝渗透系数反演问题具有较高的识别精度,反演结果可靠,可以用于实际工程。     

基于边界约束有限内存的拟牛顿CSAMT一维反演及应用

可控源音频大地电磁法（CSAMT）正演具有计算耗时、精度低的特点;常规的反演方法边界处理简单,占用内存大,搜索方向也难以精准,严重影响反演效果。为了实现CSAMT快速高精度正演,改善边界处理方法以减少内存占用,优化搜索方向以减少迭代反演次数,提出基于边界约束有限内存的拟牛顿CSAMT一维反演方法。首先,在CSAMT正演中基于高斯积分与并行算法实现了一维介质模型条件下有限长导线源激发的水平电场快速高精度计算。其次,在反演方法中,分别构建了基于相对误差和绝对误差的两种目标函数,并引入光滑模型约束,采取自适应正则化策略更新正则化因子,并采用基于边界约束的有限内存拟牛顿算法（LBFGS_B算法）,实现了CSAMT一维准确快速反演。以含低、高阻薄层的多层地电模型的水平电场E_x振幅为反演数据进行直接反演和模型检验。结果表明,该方法能够反映出地层电阻率随深度变化的趋势,并对高、低阻薄层有较好的分辨能力,低阻薄层分辨效果更佳。实际CSAMT资料的反演剖面的分辨率优于常规的连续介质反演方法,地质效果明显,表明该方法具有良好的应用前景。