用遗传算法反演一维声波介质

局部线性化的反演方法不仅在初始模远离真值时收敛速度幔，而且往往易陷入局部极大值中，而遗传算法（ＧＡ）便能解决这一问题，它是一种全局搜索法。强然它同模拟退火法一样利用转移概率进行搜索，但其收敛速度一般比模拟退火法快。本文引进了多个目标函数进行综合评价和实灾变过程，用单道地震道对一维声波介质的速度和密度同时进行了反演，反演了加噪地震道和含薄层介质模型，取得了满意的效果。     

井震联合地震道多尺度反演

多尺度（多分辨率）反演是近几年才提出的一种加快收敛速度，克服局部极小值影响，搜索全局最小极值点的反演策略，将垂向上分辨率较高的测井资料和横向上连续性较好的地震资料在地质理论指导下进行联合地震道反演，同时将小波变换多尺度分析（多分辨率分析）思想应用于上述反演中，实现对地震道多尺度（多分辨率）反演，理论和实际资料的处理均表明，这种反演方法具有收敛速度快，反演精度较高，适应能力较强的特点。     

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。     

基于自适应纯形模拟退火法一维大地电磁测深视电阻率和相位反演研究

针对大地电磁测深反演中线性化方法容易陷入局部极值,而全局优化方法收敛慢等问题,这里采用自适应纯形模拟退火综合优化方法进行大地电磁测深数据反演。该优化方法综合了下降纯形法和模拟退火法各自的优点,已被证明具有全局搜所能力和收敛速度快的特点,并且实现了视电阻率和相位同时反演,减小多解性的同时提高了反演的分辨率。通过H、K和HKH型模型的数值计算,验证了这种综合优化方法的搜索效率和全局收敛性;所有模型合成数据的反演结果都能较好地反映真实模型的结构特征。对于中间层为相对高阻的模型,虽然相对其他层结构恢复差些,但是反演后该层结构基本得到恢复。     

改进的遗传算法在堆石体参数反演中的应用

在研究堆石坝问题中,堆石体参数的合理估计非常重要,根据工程实测值反演堆石体参数不失为一种有效估计参数的新思路。在传统遗传算法操作过程中,引入模拟退火的Metropolis接受准则,并结合系统识别的基本原理改进了遗传算法。将改进的新算法应用于堆石坝主要堆石料的参数反演,结果显示其误差很小、收敛速度快、精度高的优越性,克服了传统的梯度优化方法和单纯形法所具有的搜索速度随反演参数增多呈级数减慢、容易陷入局部极值点和误差传递导致不收敛等缺点,值得在本领域参数优化中推广。     

带有横向约束的全局优化波阻抗反演方法及应用

地震波阻抗反演是一种基于地震数据得到波阻抗参数的非线性优化问题。不依赖于目标函数梯度信息的全局优化算法是求解地震波阻抗反演问题的有效方法。然而这类方法采用逐道反演的策略,忽略了相邻地震道的空间相关性,导致反演结果的横向连续性差。针对该问题,提出了一种融入旁道最优解的模型空间初始化方法约束波阻抗反演的搜索空间范围,以改善反演结果的横向连续性,进而提出了一种带有横向约束的多组变异差分进化地震波阻抗反演方法。合成地震记录算例表明,该方法相比于传统方法不仅具有更快的收敛速度,而且反演结果具有更好的横向连续性。此外,该方法被应用于胜利油田某区块的储层波阻抗参数反演,反演结果与测井资料有很好的一致性,且有效地刻画了储层砂岩厚度。     

用趋势面分析方法提高模拟退火法反演的效率

利用模拟退火反演过程中废弃的随机搜索的模型解信息, 建立起目标函数的二阶趋势面, 根据趋势面的分析结果预测和指导模拟退火的搜索方向, 使模拟退火法快速地收敛到全局最优解.数值试验证明, 这种方法比传统模拟退火法的计算效率高出十倍以上.      

围岩力学参数反演的GSA-BP神经网络模型及应用

针对遗传算法（GA）存在早熟现象和局部寻优能力较差等缺陷,引入具有很强局部搜索能力的模拟退火算法（SA）,组成改进的遗传模拟退火算法（GSA）提高优化问题的能力和求解质量。针对BP神经网络容易陷入局部最小和收敛速度慢等方面的不足,应用改进的遗传模拟退火算法搜索BP神经网络的最优权值和阀值,提高BP神经网络的预测精度,建立了围岩力学参数反分析的GSA-BP神经网络模型。将该模型应用于乌东德水电站右岸地下厂房围岩力学参数的反演分析中,根据监测围岩变形数据反演围岩力学参数,反演所得参数应用到正计算分析中,得出的计算位移与实测值吻合较好,说明该方法的有效性和应用于该工程的可行性。     

重磁位场综合优化解释方法研究及其应用

1　基于改进的模拟退火法的重磁位场反演方法在深入分析重磁三维位场场源位置函数特点的基础上,利用改进的模拟退火法实现了重磁三维位场反演,并研制开发了相应的软件。采取纵横向不等间距对地下场源空间进行剖分。这种剖分方式可起到沿深度加权的作用,极大地提高了解的稳定性。利用快速正演计算和变步长搜索方法,极大地压缩了模拟退火法与局部最优化结合中正、反演的计算量,计算表明两者的结合明显优于各自独立反演,极大地提高了计算速度。在对场源施加一定约束条件的基础上,采用基于位场特点的加权的目标函数,取得了较为精确的反演结果。…     

一种优化估计土性参数的新方法

采用改进化方向的遗传算法并结合比奥特（Ｂｉｏｔ）固结有限元数值法对一假设路堤的双层地基的一些主要土性参数进行反演,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土性参数优化估计中具有精度高、反演快的优越性,克服了传统优化方法如共轭梯度法、拟牛顿法和单纯形法所具有有的搜索速度随反演参数增多呈级数减慢、容易陷入局部极值点和误差传递导致不收敛等缺点,同时也要比简单遗传算法优     

Foundation pit dewatering optimization design based on GMW-2005 and LGR technique

The slope instability and uneven settlement caused by the foundation pit dewatering pose a great threat to the project and surrounding environment.Thus,it is necessary to carry out optimization design of the foundation pit dewatering project.To solve this problem,this paper introduces the simulation-optimization method and establishes the groundwater model based on MODFLOW-2005.The local grid refinement(LGR)technique is employed to achieve the refinement of dewatering area.Under the premise of construction safety,the minimum cost of the project is set as the objective function to solve the optimization problem of foundation pit engineering with GWM-2005.The results show that the optimization with GWM-2005 will be more accurate combined with the LGR model,but the improvement of the optimization is not obvious.It is necessary to choose a suitable local refinement model considering the engineering requirement.     

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R ²), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.     

地基承载力上限分析中的Monte Carlo搜索技术

Greco提出的临界滑裂面搜寻的Monte Carlo搜索技术具有原理简单、适应性强、搜索性能好及容易编程实现等优点,目前该类方法已在边坡稳定的优化计算中有许多成功的应用。为能够在多块体上限法求解地基极限承载力的优化计算中应用Monte Carlo搜索技术,基于上限法相容速度场的要求及地基承载力问题的特点,对Monte Carlo搜索技术实现中的目标函数、几何约束条件、初始破坏面的产生以及收敛准则等重新进行了设置。由于Monte Carlo搜索技术随机性的特点,某一次的搜索优化往往存在陷入局部极值的危险,为解决这一不足,采取了随机设置多次初始破坏面分别进行搜索优化的办法,计算表明,优化效果很好。通过对实际问题的计算以及对比发现,Monte Carlo搜索技术在此处多块体上限法求解地基承载力问题中的优化应用是相当成功的。此外,由于此处的优化是以破坏面上的节点为对象的,因此,不但可以方便地考虑三角形块体的优化,而且也可以方便地考虑四边形块体的优化问题,其适用性更强。     

新安江模型参数的线性化率定

为了解决一般概念性水文模型参数率定结果不稳定的问题,以新安江模型为例,提出了新安江模型日模参数的线性化率定方法。首先通过理想模型将该方法与SCE-UA方法及单纯形方法进行对比研究。率定结果中3种算法所获得的平均目标函数值分别为0.02、0.10、8.39 m³/s,平均循环次数分别为8、637、327,而且由线性化率定方法所获得的各参数值方差也要比其他2种方法小得多;说明线性化率定方法能够找到参数真值,计算精度和效率更高,率定结果更稳定。然后采用建阳和长滩河2个流域的实测资料对该方法进行应用检验,结果表明,同样可以较快地率定出稳定的模型参数优值。建阳流域的10组率定结果的目标函数值皆为100.35 m³/s,循环次数也皆在8次以内,而且2个流域检验期的径流深相对误差皆在9.68%以内,确定性系数皆在0.819以上。因此,线性化参数率定方法确实能够解决非线性模型参数率定结果不稳定的问题,不会产生不相关的局部参数优值,并且不受参数初值影响,计算精度高,循环次数少,是一种可行有效的全局优值参数优选方法。     

TEM最简化反演及在研究永久冻土层中的应用

阐述了基于烟圈理论的最简化反演方法和磁源TEM测深一维经验式反演方法,这两种方法可由观测数据直接反演,不需要初始模型。结合中国东北永久冻土层勘查研究课题,对TEM原始探测数据进行了反演解释。两种反演结果对揭示沿剖面横向、纵向的电性层变化,体现了较好的一致性,可为分析、划分不同电性界面的变化趋势提供参考依据。     

波动方程速度与震源函数联合反演

本文使用遗传算法对均匀弹性介质进行波速和震源函数的联合反演。通过数值计算结果表明,波速和震源函数的理论值与反演值比较吻合。方法简便,有效,并具有一定的抗噪能力。     

An approximate dynamic programming approachto decision making in the presence of uncertainty for surfactant-polymer flooding

The least squares Monte Carlo method is a decision evaluation method that can capture the effect of uncertainty and the value of flexibility of a process. The method is a stochastic approximate dynamic programming approach to decision making. It is based on a forward simulation coupled with a recursive algorithm which produces the near-optimal policy. It relies on the Monte Carlo simulation to produce convergent results. This incurs a significant computational requirement when using this method to evaluate decisions for reservoir engineering problems because this requires running many reservoir simulations. The objective of this study was to enhance the performance of the least squares Monte Carlo method by improving the sampling method used to generate the technical uncertainties used in obtaining the production profiles. The probabilistic collocation method has been proven to be a robust and efficient uncertainty quantification method. By using the sampling methods of the probabilistic collocation method to approximate the sampling of the technical uncertainties, it is possible to significantly reduce the computational requirement of running the decision evaluation method. Thus, we introduce the least squares probabilistic collocation method. The decision evaluation considered a number of technical and economic uncertainties. Three reservoir case studies were used: a simple homogeneous model, the PUNQ-S3 model, and a modified portion of the SPE10 model. The results show that using the sampling techniques of the probabilistic collocation method produced relatively accurate responses compared with the original method. Different possible enhancements were discussed in order to practically adapt the least squares probabilistic collocation method to more realistic and complex reservoir models. Furthermore, it is desired to perform the method to evaluate high-dimensional decision scenarios for different chemical enhanced oil recovery processes using real reservoir data.     

A hybrid method for grade estimation using genetic algorithm and neural networks

In the present paper, a new hybrid method is proposed for grade estimation. In this method, the multilayer perceptron (MLP) network is trained using the combination of the Levenberg–Marquardt (LM) method and genetic algorithm (GA). Having a few samples for grade estimation, it is difficult to get a proper result using some function approximation methods like neural networks or geostatistical methods. The neural network training methods are very sensitive to initial weight values when there are a few samples as a training dataset. The main objective of the proposed method is to resolve this problem. Here, our method finds the optimal initial weights by combining GA and LM method. Having the optimal initial values for weights, the local minima are avoided in the training phase and subsequently the neural network sustainability is trained optimally. Furthermore, the hybrid method is applied for grade estimation of Gol-e-Gohar iron ore in south Iran. The proposed method shows significant improvements compared to both conventional MLP and Kriging method. The efficiency of the proposed method gets more highlighted when the training data set is small.     

有限长导线源频率测深有限内存拟牛顿一维反演

本文采用有限内存拟牛顿法实现有限长导线源频率测深阻抗响应数据的一维反演。水平层状介质有限长导线源阻抗频率响应由基于虚界面法获得的地表水平正交电场和磁场计算得到;一维反演优化问题的求解利用有限内存拟牛顿法,结合光滑模型约束,直接对阻抗的频率响应数据进行反演。在反演过程中,正则化参数的调整采用目标函数自适应技术。反演模型剖分为多层,各层厚度自地表按比例增加。反演从均匀半空间开始,终止条件为目标函数相对变化小于10~(-4)。分别对理论模型和实际数据进行了反演模拟。为考察反演的稳定性,还对理论数据添加10%随机噪声后进行了反演。数值计算结果表明:有限内存拟牛顿方法可以用于有限长导线源频率测深阻抗频率响应的反演;该反演方法对初始模型的依赖性弱,从均匀半空间模型出发基本可以恢复到真实模型;反演初期收敛较快,后期收敛速度变慢,反演结束一般需要迭代40次左右。噪声数据反演结果表明,随机噪声对反演结果影响不大,说明有限内存拟牛顿法具有较好的抗干扰能力。本文研究成果给出了可控源电磁数据反演的一种新方法;同时,利用本文的研究成果,可以为二维或三维反演建立合适的初始模型。     

煤层气损失气量计算的全脱分析法

针对目前煤层气损失气量无法计算煤层的溶解气和游离气含量问题,根据烃类气体进入钻井液的方式和分布状态理论,基于气测录井资料,提出了计算煤层气损失气量的新方法——全脱分析法。通过沁水盆地枣园区块3号煤层的实际应用,发现该方法计算的损失气量明显高于常规煤心样品的回归结果,前者一般为后者的1.5~4.5倍。该方法不仅能有效地解决煤层气损失气量计算中忽视的溶解气和游离气问题,而且还能消除损失时间的影响,同时也可以按不同部位计算煤层气损失气量。该方法适用于不同煤级、各种结构的煤岩,数据易采集,计算过程简单,有很好的应用前景。