波阻抗遗传算法反演方法的研究及应用

作者介绍的遗传算法是在自然界生物遗传过程优选和进化原理的基础上，针对波阻抗反演的实际问题，对标准的遗传算法进行了改进，引入了模拟退火算法中的热槽法控制初始群体的产生，用逐位迭代的交换方式代替传统的杂交方式即随机选择杂交点进行交换。使用改进的遗传算法对地震波阻抗进行反演，可很好地提高反演的精度。在文中最后，作者通过理论模型和实例验证了上述的思想。     

基于十进制遗传算法的含水层参数的反演

含水层参的反演是一个复杂的非线性优化问题，针对传统二进制遗传算法收敛性能差的缺陷，提出了反演含水层参数的十进制遗传算法.以直线隔水边界附近的井流模型为例，讨论了十进制遗传算法在含水层参数反演中的应用，并与二进制遗传算法的进行比较.结果表明，该方法在含水层参数的反演中不仅是可行的，而且具有较好的确定性和较高的精度;与二进制遗传算法相比，十进制遗传算法的收敛性较好，省时高效，且表示较为自然，容易引入相关领域知识.同时，结合实例的分析结果得出种群的规模对算法的收敛性没有明显的影响。     

基于改进遗传算法的双感应测井反演

针对双感应测井受到泥浆侵入和围岩等因素影响,采用小生境技术的改进遗传算法对双感应测井进行原状地层电阻率和侵入半径等参数反演。利用多层层状地层模型对算法进行验证,反演结果能较好地反映地层模型参数。实例资料的应用结果表明,改进遗传算法反演出的地层电阻率使测井解释结论更加接近试油结论。     

应用改进的遗传算法反演多层密度界面

利用具有全局优化功能的遗传算法直接反演多层密度界面.首先根据重力反演的特点对遗传算法进行改进, 使遗传算法基因交换过程中交换位置的确定同重力异常的拟合情况相结合, 给出适合于重力反演特点的遗传算法.然后利用改进后的遗传算法直接反演多层密度界面.理论模型和实际剖面的计算表明改进是有效的.      

改进的遗传算法在电测深反演中的应用

遗传算法是地球物理数据非线性反演中常用的方法,但在使用中所需内存较多,容易"早熟"或陷入局部极小。为了在电阻率测深反演中更好地使用遗传算法,提出了利用统计学建模获得精细初始模型后,再利用改进的遗传算法反演。改进了传统遗传算法评价解的方法、增加了局部目标函数并限制遗传基因的繁殖方向。对理论模型的反演计算证明改进的遗传算法可以减少计算量,改善电阻率测深的反演效果。     

用遗传算法反演各向异性介质弹性参数

研究了用遗传算法来反演各向异性介质的弹性常数。首先概述遗传算法的基本原理,然后以横向各向同性介质的一维模型反演为例,重点讨论了交换概率、更新概率、变异概率和控制温度对反演收敛速度的影响。为改进收敛性能,反演中利用模拟退火法中的控制温度对目标函数作尺度变换,并采用了层剥离技术。对有噪及二维情形也作了考虑。计算表明遗传算法反演是一种良好的非线性反演方法。     

二维密度界面的遗传算法反演

二维密度界面的反演可表述成非线性优化问题，本文用遗传算法进行反演计算，首先把连续的密度界面分割成若干单元，在单元中用形函数拟合，再用高斯积分求解重力异常值，把约束条件和拟合方差组合成目标函数，用园柱体和二维盆地模型的反演实例表明，用遗传算法，反演二维密度界面是可行的     

层状介质大地电磁的自适应量子遗传反演法

将量子遗传算法引入到层状介质大地电磁数据的反演, 得到了层状介质的大地电磁量子遗传反演法.数值试验结果发现该算法仍然存在较严重的早熟收敛现象.为此, 将自适应思想引入到量子遗传算法中来, 通过动态调整量子遗传算法的模型搜索空间, 建立了一种新的改进型量子遗传算法——自适应量子遗传算法, 使算法在迭代过程中能自适应地寻找模型最优值.通过典型测试函数和层状介质大地电磁模型数值试验, 结果表明, 改进算法有效压制了常规量子遗传算法的早熟收敛性, 提高了算法的搜索效率和反演效果.采用该算法对实际的大地电磁资料进行了处理, 取得了较好的地质效果.      

基于改进遗传算法的河南地区水文地质参数反演计算研究

利用改进遗传算法对河南地区水文地质参数进行反演计算,并结合抽水试验方式对反演参数的精度进行分析。结果表明:改进遗传算法可使得水文地质参数取得局部最优解,且加速收敛速度,通过抽水试验分析,两个参数的反演误差均在15%以内,且较传统算法有明显改善。研究成果对于河南地区水文地质参数反演具有重要的方法参考价值。     

遗传算法在边坡数值计算中的应用

改进了进化方向的遗传算法与有限元数值法，结合并研制了相应的软件。应用该软件对多类型岩土边坡进行弹性模量、内聚力、内摩擦角等参数反演分析，显示误差很小，收敛速度也很快，这说明改进进化方向遗传算法这种新型的优化算法在多类型岩土参数优化估计中具有独特的优势。     

遗传算法在反演三维地下水流模型参数中的应用

本文以非均质各向同性承压三维非稳定流为理想模型，结合有限元法讨论了用遗传算法反演水文地质参数问题，计算结果表明，本文在瘴简单遗传算法（SGA）的基础上提出的优体克隆＋子体优生遗传算法（BCC－YGCD－GA）具有收敛速度快，解的精度高和避免出现早期等优点，在水资源评价和矿床疏干计算中有广阔的应用前景。     

Feasibility Analysis of the Use of Binary Genetic Algorithms as Importance Samplers Application to a 1-D DC Resistivity Inverse Problem

DC resistivity inverse problems are ill-posed. For the Vertical Electrical Sounding method the acceptable solutions lie in very narrow elongated-shape regions in the model space. To characterize this ensemble of solutions is a central question. In a Bayesian framework this issue is solved adopting as solution the so-called model posterior probability distribution. However, due to the nonlinearity of the problem, this distribution is not explicitly known, or it is difficult to calculate. Therefore, algorithms that efficiently sample the model space according to it (importance sampling) are very desirable. The main goal of this paper is to numerically explore the performance of binary genetic algorithms as posterior importance sampling strategies. Their behavior will be firstly analyzed using 2D synthetic posterior test functions bearing the relevant properties of the real geo-electrical inverse problem. The conclusions will be again checked through the histogram reconstruction of parameters in a synthetic VES case, and eventually, in a real, higher dimensional, sea-water coastal intrusion problem, by comparing the results with those obtained with a theoretically correct Metropolis-Hasting importance sampler (simulated annealing without cooling). Percentile curves are introduced as an effective tool for risk assessment. We show that binary genetic algorithms perform well under very general assumptions. When the roulette wheel is the selection method used, mutation is over 10%, and the algorithm does not incorporate elitism. The results do not depend on the values of the remaining tuning parameters. Finally, to improve the efficiency of the sampling strategy, we introduce a binary genetic algorithm with oriented search space. This is done with the help of linearization of the forward operator and singular value decomposition around the maximum posterior estimate. It is shown, also, that the logarithmic model parameterization is adequate for this task.     

改进的遗传算法在地下水数值模拟中的应用

地下水流数值模拟中的模型识别问题，可以转化为函数的最优化问题。鉴于遗传算法的特点，将之引入到地下水流数值法中，用以解决地下水数学模型的识别问题。在建立地下水数值模拟中模型识别问题的是优化模型后，采取将最优化模型中的目标函数嵌入到遗传算法适应度函数中的方法，实现遗传算法与地下水流数值法的耦合。基于优化模型和遗传算法的运算过程，编写计算程序，实现地下水数学模型的自动识别。根据在珲春盆地地下水资源评价实例中应用得到的结果，信纸证了改进的遗传算法在地下水数值模拟中应用的可行性与有效性。     

基于改进遗传算法的基桩缺陷自动识别

利用遗传算法对基桩缺陷自动识别的方法进行了研究。针对简单遗传算法的早熟和后期收敛缓慢现象,引入了自适应杂交变异概率方法和自适应小生境技术的方法,并在由一维应力波理论和差分方法对低应变测桩信号仿真的基础上,对基桩缺陷自动识别进行了研究。结果表明,该算法适用于桩缺陷自动识别,可以有效地解决简单遗传算法的早熟和后期收敛缓慢现象,而且计算效率也有明显提高。     

A Frequency Matching Method: Solving Inverse Problems by Use of Geologically Realistic Prior Information

The frequency matching method defines a closed form expression for a complex prior that quantifies the higher order statistics of a proposed solution model to an inverse problem. While existing solution methods to inverse problems are capable of sampling the solution space while taking into account arbitrarily complex a priori information defined by sample algorithms, it is not possible to directly compute the maximum a posteriori model, as the prior probability of a solution model cannot be expressed. We demonstrate how the frequency matching method enables us to compute the maximum a posteriori solution model to an inverse problem by using a priori information based on multiple point statistics learned from training images. We demonstrate the applicability of the suggested method on a synthetic tomographic crosshole inverse problem.     

基于遗传算法的扩散波洪水演算参数的确定方法

大量的理论分析和实际洪水演算表明,扩散波方法是一种既具有足够精度又相对简单的一类洪水演算方法。以线性扩散波方程在自由下边界条件下的解析解为例,利用遗传算法来确定扩散波方程的参数。遗传算法和一般的优化算法不同,它具有全局寻优能力,一般能得到问题的最优解或准最优解,是一类优秀的非线性函数优化方法。实例计算结果表明,采用遗传算法所得到的参数精度较高,可以类推到其它水文模型的参数率定中去。     

Comparing the Gradual Deformation with the Probability Perturbation Method for Solving Inverse Problems

Inverse problems are ubiquitous in the Earth Sciences. Many such problems are ill-posed in the sense that multiple solutions can be found that match the data to be inverted. To impose restrictions on these solutions, a prior distribution of the model parameters is required. In a spatial context this prior model can be as simple as a Multi-Gaussian law with prior covariance matrix, or could come in the form of a complex training image describing the prior statistics of the model parameters. In this paper, two methods for generating inverse solutions constrained to such prior model are compared. The gradual deformation method treats the problem of finding inverse solution as an optimization problem. Using a perturbation mechanism, the gradual deformation method searches (optimizes) in the prior model space for those solutions that match the data to be inverted. The perturbation mechanism guarantees that the prior model statistics are honored. However, it is shown with a simple example that this perturbation method does not necessarily draw accurately samples from a given posterior distribution when the inverse problem is framed within a Bayesian context. On the other hand, the probability perturbation method approaches the inverse problem as a data integration problem. This method explicitly deals with the problem of combining prior probabilities with pre-posterior probabilities derived from the data. It is shown that the sampling properties of the probability perturbation method approach the accuracy of well-known Markov chain Monte Carlo samplers such as the rejection sampler. The paper uses simple examples to illustrate the clear differences between these two methods     

Using a genetic algorithm for 3-D inversion of gravity data in Fuerteventura (Canary Islands)

The use of genetic algorithms in geophysical inverse problems is a relatively recent development and offers many advantages in dealing with the non-linearity inherent in such applications. We have implemented a genetic algorithm to efficiently invert a set of gravity data. Employing several fixed density contrasts, this algorithm determines the geometry of the sources of the anomaly gravity field in a 3-D context. The genetic algorithms, based on Darwins theory of evolution, seek the optimum solution from an initial population of models, working with a set of parameters by means of modifications in successive iterations or generations. This searching method traditionally consists of three operators (selection, crossover and mutation) acting on each generation, but we have added a further one, which smoothes the obtained models. In this way, we have designed an efficient inversion gravity method, confirmed by both a synthetic example and a real data set from the island of Fuerteventura. In the latter case, we identify crustal structures related to the origin and evolution of the island. The results show a clear correlation between the sources of gravity field in the model and the three volcanic complexes recognized in Fuerteventura by other geological studies.