地球物理资料群体智能反演(英文)

复杂地球物理资料的反演问题往往是一个求解多参数非线性多极值的最优解问题。而鸟和蚂蚁等群体觅食的过程,正好与寻找地球物理反演最优解的过程相似。基于自然界群体协调寻优的思想,本文提出了交叉学科的群体智能地球物理资料反演方法,并给出了其对应的数学模型。用一个有无限多个局部最优解的已知模型对该类方法进行了试验。然后,将它们应用到了不同的复杂地球物理反演问题中:(1)对噪声敏感的线性问题;(2)非线性和线性同步反演问题;(3)非线性问题。反演结果表明,群体智能反演是可行的。与常规遗传算法和模拟退火法相比,该类方法有收敛速度相对快、收敛精度相对高等优点;与拟牛顿法和列文伯格一马夸特法相比,该类方法有能跳出局部最优解等优点。     

A Stochastic Inversion Method for Potential Field Data: Ant Colony Optimization

Simulating natural ants’ foraging behavior, the ant colony optimization (ACO) algorithm performs excellently in combinational optimization problems, for example the traveling salesman problem and the quadratic assignment problem. However, the ACO is seldom used to inverted for gravitational and magnetic data. On the basis of the continuous and multi-dimensional objective function for potential field data optimization inversion, we present the node partition strategy ACO (NP-ACO) algorithm for inversion of model variables of fixed shape and recovery of physical property distributions of complicated shape models. We divide the continuous variables into discrete nodes and ants directionally tour the nodes by use of transition probabilities. We update the pheromone trails by use of Gaussian mapping between the objective function value and the quantity of pheromone. It can analyze the search results in real time and promote the rate of convergence and precision of inversion. Traditional mapping, including the ant-cycle system, weaken the differences between ant individuals and lead to premature convergence. We tested our method by use of synthetic data and real data from scenarios involving gravity and magnetic anomalies. The inverted model variables and recovered physical property distributions were in good agreement with the true values. The ACO algorithm for binary representation imaging and full imaging can recover sharper physical property distributions than traditional linear inversion methods. The ACO has good optimization capability and some excellent characteristics, for example robustness, parallel implementation, and portability, compared with other stochastic metaheuristics.     

Ant colony optimization of tuned mass dampers for earthquake oscillations of high-rise structures including soil–structure interaction

This paper investigates the optimized parameters for tuned mass dampers (TMDs) to decrease the earthquake vibrations of tall buildings; involving soil–structure interaction (SSI) effects. The time domain analysis based on Newmark method is employed in this study. To illustrate the results, Tabas and Kobe earthquakes data are applied to the model, and ant colony optimization (ACO) method is utilized to obtain the best parameters for TMD. The TMD mass, damping coefficient and spring stiffness are assumed as design variables, and the objective is to reduce both the maximum displacement and acceleration of stories. It is shown that how the ACO can be effectively applied to design the optimum TMD device. It is also indicated that the soil type greatly affects the TMD optimized parameters and the time response of structures. This study helps the researchers to better understanding of earthquake vibrations, and leads the designers to achieve the optimized TMD for high-rise buildings.     

基于最佳摄动量法的桩基承载力反演

通过高应变动力试桩法来获得桩土参数是目前比较流行的试桩方法,其原理主要是通过以桩顶的实测信息来反演桩土参数,其中比较成熟的CAPW APC法主要用试凑法来反演桩土模型参数,其反演结果呈现一定的随机性且正确性过于依赖操作者经验。本文尝试以CAPW APC法所采用比较成熟的桩土模型为基础,引入基于最佳摄动量法的局部优化方法来反演桩土参数。在弹性波波动方程隐式差分的基础上,对反演参数采用摄动展开并推导出参数反演的递推公式,最后给出合适的反演算法。考虑到初始参数对局部优化方法的影响,根据场地的实际情况选取合适的初始模型,计算显示可以获得比较理想的结果,说明本法是对桩基动测方法的一种有效的新尝试。     

基于混合优化算法的MT阻抗张量畸变分解方法

在前人研究基础上,对Groom-Bailey(GB)张量分解畸变因子和区域阻抗的求解方法进行了改进.首先,通过Swift旋转与GB分解的扭变和剪切矩阵的求逆变换,利用变换后区域阻抗主对角元素为0的条件获得关于扭变因子和剪切因子的超定方程组,采用模拟退火全局优化算法进行求解.其次,由得到的扭变因子和剪切因子,结合Swift旋转确定的走向角和区域阻抗元素的估计,作为非线性最小二乘局部优化算法的初始值,对GB分解定义式的超定方程组进行求解,得到各畸变参数和区域阻抗的解.通过模型试验验证了方法的正确,对方法的稳定性进行了比较与评价,并通过与已有结果的对比和实际资料的应用,表明了方法实际应用的效果.     

基于ACCRBF网络的多层砖房震害预测

针对传统震害预测方法逐栋抽样计算建筑物抗震性能的不足,本文提出了一种基于蚁群聚类径向基(ACCRBF)网络模型的建筑物震害预测方法。依据不同地震动峰值加速度下多层砖房的实际震害资料,对模型进行训练,在模型的输入和输出之间建立映射关系,并利用这种映射关系对未知样本进行分类,实现对多层砖房的震害分析和预测。模型的输入为反映结构的震害影响因子,输出为给定的地震动峰值加速度下结构震害等级。研究表明,基于ACCRBF网络模型的多层砖房震害预测结果与震害实例基本吻合,具有推广应用价值。     

蚁群算法在地表隐伏断裂识别中的应用的探讨——以九寨沟景区为例

在发震地区,准确识别地表断裂带可为地质与地震灾害预测提供基础数据。由于断裂带一般在地表出露是不连续的,在此对基于蚁群算法的地表断裂隐伏段的识别研究进行了尝试,基于遥感图像及航测数据,获取地形、地貌要素以及可以参考的其他要素,使用改进最短对角线方法提取断层表面的图像要素特征作为识别母体,在算法上依次将断裂带三角面片全部配对连接成四面体,再采用蚁群算法进行地表隐伏断裂空间识别。在九寨沟景区中进行了实例探讨,对2017年九寨沟地震的断裂进行尝试性识别,认为该方法可用来初步作为辅助识别地表断裂时的一个参考,同时基于本文方法与其他两种识别方法进行了分析对比。     

Parameter identification for leaky aquifers using global optimization methods

In the past, graphical or computer methods were usually employed to determine the aquifer parameters of the observed data obtained from field pumping tests. Since we employed the computer methods to determine the aquifer parameters, an analytical aquifer model was required to estimate the predicted drawdown. Following this, the gradient‐type approach was used to solve the nonlinear least‐squares equations to obtain the aquifer parameters. This paper proposes a novel approach based on a drawdown model and a global optimization method of simulated annealing (SA) or a genetic algorithm (GA) to determine the best‐fit aquifer parameters for leaky aquifer systems. The aquifer parameters obtained from SA and the GA almost agree with those obtained from the extended Kalman filter and gradient‐type method. Moreover, all results indicate that the SA and GA are robust and yield consistent results when dealing with the parameter identification problems. Copyright © 2006 John Wiley & Sons, Ltd.     

Improving the efficiency of ant algorithms using adaptive refinement: Application to storm water network design

The ant algorithm is a new evolutionary optimization method proposed for the solution of discrete combinatorial optimization problems. Many engineering optimization problems involve decision variables of continuous nature. Application of the ant algorithm to the optimization of these continuous problems requires discretization of the continuous search space, thereby reducing the underlying continuous problem to a discrete optimization problem. The level of discretization of the continuous search space, however, could present some problems. Generally, coarse discretization of the continuous design variables could adversely affect the quality of the final solution while finer discretization would enlarge the scale of the problem leading to higher computation cost and, occasionally, to low quality solutions. An adaptive refinement procedure is introduced in this paper as a remedy for the problem just outlined. The method is based on the idea of limiting the originally wide search space to a smaller one once a locally converged solution is obtained. The smaller search space is designed to contain the locally optimum solution at its center. The resulting search space is discretized and a completely new search is conducted to find a better solution. The procedure is continued until no improvement can be made by further refinement. The method is applied to a benchmark problem in storm water network design discipline and the results are compared with those of existing methods. The method is shown to be very effective and efficient regarding the optimality of the solution, and the convergence characteristics of the resulting ant algorithm. Furthermore, the method proves itself capable of finding an optimal, or near-optimal solution, independent of the discretization level and the size of the colony used.     

Effects of uncertainty in rock-physics models on reservoir parameter estimation using seismic amplitude variation with angle and controlled-source electromagnetics data

This paper investigates the effects of uncertainty in rock-physics models on reservoir parameter estimation using seismic amplitude variation with angle and controlled-source electromagnetics data. The reservoir parameters are related to electrical resistivity by the Poupon model and to elastic moduli and density by the Xu-White model. To handle uncertainty in the rock-physics models, we consider their outputs to be random functions with modes or means given by the predictions of those rock-physics models and we consider the parameters of the rock-physics models to be random variables defined by specified probability distributions. Using a Bayesian framework and Markov Chain Monte Carlo sampling methods, we are able to obtain estimates of reservoir parameters and information on the uncertainty in the estimation. The developed method is applied to a synthetic case study based on a layered reservoir model and the results show that uncertainty in both rock-physics models and in their parameters may have significant effects on reservoir parameter estimation. When the biases in rock-physics models and in their associated parameters are unknown, conventional joint inversion approaches, which consider rock-physics models as deterministic functions and the model parameters as fixed values, may produce misleading results. The developed stochastic method in this study provides an integrated approach for quantifying how uncertainty and biases in rock-physics models and in their associated parameters affect the estimates of reservoir parameters and therefore is a more robust method for reservoir parameter estimation.     

Risk assessment using suprema data

This paper proposes a stochastic approach to model temperature dynamic and study related risk measures. The dynamic of temperatures can be modelled by a mean-reverting process such as an Ornstein–Uhlenbeck one. In this study, we estimate the parameters of this process thanks to daily observed suprema of temperatures, which are the only data gathered by some weather stations. The expression of the cumulative distribution function of the supremum is obtained thanks to the law of the hitting time. The parameters are estimated by a least square method quantiles based on this function. Theoretical results, including mixing property and consistency of model parameters estimation, are provided. The parameters estimation is assessed on simulated data and performed on real ones. Numerical illustrations are given for both data. This estimation will allow us to estimate risk measures, such as the probability of heat wave and the mean duration of an heat wave.     

Towards large-scale stochastic refraction tomography: a comparison of three evolutionary algorithms

The main goal of this study is to assess the potential of evolutionary algorithms to solve highly non-linear and multi-modal tomography problems (such as first arrival traveltime tomography) and their abilities to estimate reliable uncertainties. Classical tomography methods apply derivative-based optimization algorithms that require the user to determine the value of several parameters (such as regularization level and initial model) prior to the inversion as they strongly affect the final inverted model. In addition, derivative-based methods only perform a local search dependent on the chosen starting model. Global optimization methods based on Markov chain Monte Carlo that thoroughly sample the model parameter space are theoretically insensitive to the initial model but turn out to be computationally expensive. Evolutionary algorithms are population-based global optimization methods and are thus intrinsically parallel, allowing these algorithms to fully handle available computer resources. We apply three evolutionary algorithms to solve a refraction traveltime tomography problem, namely the differential evolution, the competitive particle swarm optimization and the covariance matrix adaptation–evolution strategy. We apply these methodologies on a smoothed version of the Marmousi velocity model and compare their performances in terms of optimization and estimates of uncertainty. By performing scalability and statistical analysis over the results obtained with several runs, we assess the benefits and shortcomings of each algorithm.     

Discovery of transition rules for geographical cellular automata by using ant colony optimization

A new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the core of CA models is how to define transition rules. However, most of the transition rules are defined by mathematical equations, and are hence not explicit. When the study area is complicated, it is much more difficult to extract parameters for geographical CA. As a result, ACO is applied to geographical CA to automatically and intelligently obtain transition rules in this paper. The transition rules extracted by ACO are defined as logical expressions rather than implicit mathematical equations to describe the complex relationships of the nature, and easy for people to understand. The ACO-CA model was applied to simulating rural-urban land conversions in Guangzhou City, China, and appropriate simulation results were generated. Compared with See5.0 decision tree model, ACO-CA is more suitable to discovering transition rules for geographical CA.     

3D Seismic survey design using mixed-radix based algorithm inversion

The determination of three-dimensional geometry and acquisition parameters, the seismic acquisition survey design, is constantly subject of studies in obtaining data with the highest seismic quality, operational efficiency and cost minimization. In this paper, we propose a methodology for inverting geometry parameters of three-dimensional orthogonal land seismic surveys based on a direct search method using a mixed-radix based algorithm. In this algorithm, the search space is discretized on a mixed-radix base, which depends on the extreme values and the search resolution of each parameter. We will show how to reparametrize the orthogonal acquisition geometry elements in order to obtain the independents and integers parameters that are necessary to construct the mixed-radix base. For the optimization purpose, we define an objective function to contemplate target parameters associated with the elements of the acquisition geometry directly related to the geophysical and operational constraints. Taking in account that the mathematical functions and the objective function we define for the problem have no significant computational cost, all model space parameters are fast and efficiently tested. We applied the algorithm, using as input data, provided by a one-line roll orthogonal reference geometry, assuming a pair of geological objectives as shallow and deep targets. All selected models that meet both the proposed objectives and the constraints are organized by decreasing order of fitness so that with the mixed-radix inversion algorithm we found not only the best model, but also a set of suitable models. Likewise, with the best set of geometries, it is possible to establish a direct comparison between them, analysing their adherence to the technical and operational requirements according to the availability and degree of detail of each one. We show the top 10 best results as a table, allowing a direct comparison between all aspects of these geometries, and we summarize the results showing graphically the fitness of all selected geometries and the inverted geometry elements for the 1000 best geometries. These graphical displays provide a direct way to understand how each model behaves as the fitness decreases. The algorithm is very flexible and its application can be extended to any environment and type of acquisition geometry, and in any phase study of an area be it regional, exploratory or development.     

储层弹性与物性参数地震叠前同步反演的确定性优化方法

储层弹性与物性参数可直接应用于储层岩性预测和流体识别,是储层综合评价和油气藏精细描述的基本要素之一.现有的储层弹性与物性参数地震同步反演方法大都基于Gassmann方程,使用地震叠前数据,通过随机优化方法反演储层弹性与物性参数;或基于Wyllie方程,使用地震叠后数据,通过确定性优化方法反演储层弹性与物性参数.本文提出一种基于Gassmann方程、通过确定性优化方法开展储层弹性和物性参数地震叠前反演的方法,该方法利用Gassmann方程建立储层物性参数与叠前地震观测数据之间的联系,在贝叶斯反演框架下以储层弹性与物性参数的联合后验概率为目标函数,通过将目标函数的梯度用泰勒公式展开得到储层弹性与物性参数联合的方程组,其中储层弹性参数对物性参数的梯度用差分形式表示,最后通过共轭梯度算法迭代求解得到储层弹性与物性参数的最优解.理论试算与实际资料反演结果证明了方法的可行性.     

Partially constrained ant colony optimization algorithm for the solution of constrained optimization problems: Application to storm water network design

This paper exploits the unique feature of the Ant Colony Optimization Algorithm (ACOA), namely incremental solution building mechanism, to develop partially constraint ACO algorithms for the solution of optimization problems with explicit constraints. The method is based on the provision of a tabu list for each ant at each decision point of the problem so that some constraints of the problem are satisfied. The application of the method to the problem of storm water network design is formulated and presented. The network nodes are considered as the decision points and the nodal elevations of the network are used as the decision variables of the optimization problem. Two partially constrained ACO algorithms are formulated and applied to a benchmark example of storm water network design and the results are compared with those of the original unconstrained algorithm and existing methods. In the first algorithm the positive slope constraints are satisfied explicitly and the rest are satisfied by using the penalty method while in the second one the satisfaction of constraints regarding the maximum ratio of flow depth to the diameter are also achieved explicitly via the tabu list. The method is shown to be very effective and efficient in locating the optimal solutions and in terms of the convergence characteristics of the resulting ACO algorithms. The proposed algorithms are also shown to be relatively insensitive to the initial colony used compared to the original algorithm. Furthermore, the method proves itself capable of finding an optimal or near-optimal solution, independent of the discretisation level and the size of the colony used.     

热带气旋风场模型构造及特征参数估算

探讨了利用气旋风场分布的经验模型估算热带气旋尺度(8级大风圈半径)的方法.用美国联合台风警报中心整编的2001年西北太平洋热带气旋的“最佳尺度”资料,确定了各模型的经验常数,并计算了各模型的估算精度.结果表明,“VBogus”模型能获得热带气旋(Tropical Cyclone,简称TC)尺度的较好估算.基于“VBogus”模型,通过拟合热带气旋尺度的非对称分布,构造了能描述热带气旋非对称风场的"修正VBogus"模型,并估算了该模型中各参数在不同季节和不同地理区域的取值,为热带气旋尺度变化和非对称结构机制等问题的研究和应用提供新依据.     

叠前弹性参数一致性反演方法研究(英文)

常规叠前反演中,纵波速度、横波速度和密度三参数之间,在反演精度上存在明显的差异,"三参数"的一致性反演遂成为重要的研究目标。本文从导致它们精度差异的根源入手,提出了新的叠前反演算法和思路,通过合理的近似,构成参数间的互动和相互约束,使三个参数的反演精度得以同步提高。理论模型试算和实际资料应用表明,三个弹性参数均有较高的反演精度且保持了一致性,与理论模型和实际资料吻合。该方法具有较好的应用前景。     

基于改进蚁群算法在面波频散曲线反演中的应用

为快速准确的反演得到近地表地层结构,将一种新颖而强大的非线性算法——蚁群算法引入到瑞雷波频散曲线领域,并对其进行相应的改进,改进蚁群算法的优点是运算效率快、精度高、算法简单、灵活易于实现,需要调节控制参数也较少。文中分别在无噪声\,含噪声以及实测数据进行反演测试,通过模型数据和实测数据表明,应用于瑞雷波反演中的改进蚁群算法在收敛速度与收敛精度之间能达到良好的平衡,所得解具有较高可信度。而且算法为促进所得解快速收敛到全局最优,在搜索中分全局搜索与局部搜索两个方式进行,能够有效地避免局部最优解产生。借助人工合成的瑞雷波数据以及真实观测数据,验证了改进蚁群算法在反演近地表剪切波速度时的有效性和通用性。此外,文中与遗传算法进行比较,得出改进蚁群算法具有高效性和高精度性的优点。     

Multi-parameter nonlinear inversion with exact reflection coefficient equation

Amplitude variation with amplitude or angle (AVO/AVA) inversion has been widely utilized in exploration geophysics to estimate the formation of elastic parameters underground. However, conventional AVO/AVA inversion approaches are based on different approximate equations of Zoeppritz equations under various hypotheses, such as limited incident angles or weak property contrast, which reduces their prediction precision theoretically. This study combines the exact P-wave Zoeppritz equation with a nonlinear direct inversion algorithm to estimate the six parameters imbedded in the exact equation simultaneously. A more direct and explicit expression of the Zoeppritz equation is discussed in the case of P-wave exploration, under which condition the incident longitudinal wave produces the reflected longitudinal (P–P) wave and upgoing converted shear (P–SV) wave. Utilizing this equation as the forward solver, a nonlinear direct inversion method is introduced to implement the direct inversion of the six parameters including P-wave velocities, S-wave velocities, and densities in the upper and lower media around an interface, respectively. This nonlinear algorithm is able to estimate the inverse of the nonlinear function in terms of model parameters directly rather than in a conventional optimization way. Model tests illustrate that the nonlinear direct inversion method shows great potential to estimate multiple parameters with the exact Zoeppritz equation.