集成遗传算法及BP算法的潜在震源区划分

以华南沿海地区为例，集成遗传算法与BP算法进行潜在震源区的划分. 用遗传算法辅助人工神经网络的设计，在无限的解空间中快速找到人工神经网络的最佳参数组合. 结果表明:由该分类系统划分出的不同震级上限的潜在震源区分布，反映了华南沿海地区地震环境与地震发生的内在规律性，从而减少了人的主观判断影响.      

基于GA-BP理论的储层视裂缝密度地震非线性反演方法

基于GA-BP理论,将自适应遗传算法与人工神经网络技术(BP算法)有机地相结合,形成了一种储层裂缝自适应遗传-神经网络反演方法.这种新的方法是由编码、适应度函数、遗传操作及混合智能学习等组成,即在成像测井裂缝密度数据约束下,通过对目标问题进行编码(称染色体),然后对染色体进行选择、交叉和变异等遗传操作,使染色体不断进化,从而快速获得全局最优解.在反演执行过程中,利用地震数据和成像测井裂缝密度数据之间的非线性映射关系建立训练样本,将GA算法与BP算法有机地结合,优化三层前向网络参数;或将GA与ANFIS相结合,优化ANFIS网络参数.并采用GA算法与TS算法(Tabu Search)相结合的自适应混合学习算法,该学习算法自始至终将GA和BP两种算法按一定的概率比例进行,其概率自适应变化,以达到混合算法的均衡.这种混合算法提高了网络的收敛速度和精度.我们分别利用两个研究地区的6井和1井成像测井裂缝密度数据与地震数据之间的非线性映射关系建立训练样本,对过这两口井的测线的地震数据进行反演,获得了视裂缝密度剖面,视裂缝密度剖面上裂缝分布特征符合沉积相分布特征和岩石力学性质的变化特征.这种视裂缝密度剖面含有储层裂缝的定量信息,其误差可为油气勘探开发实际要求所允许.因此,这种新的方法优于只能作裂缝定性分析的常规裂缝地震预测方法,具有广阔的应用前景.     

微种群遗传算法优化结构振动控制

本文将微种群遗传算法应用到结构振动控制中，用遗传算法优化控制器，以解决一类用经典线性反馈控制无法解决的半主动控制的优化问题，该方法提出将非线性控制问题线性化，导出了简化过程，然后利用遗传算法求解，它具有利用微种群遗传算法全局寻优，并且对目标函数的性态要求较少的特点，数值算例表明，本文方法是有效的。     

Genetic algorithm for seismic topology optimization of lifeline network systems

Lifeline systems, such as water distribution and gas supply networks, usually cover large areas. For these systems, seismic design is always a difficult problem because of the complexity of large‐scale networks. In this paper, a topology optimization technology for lifeline networks is established. Firstly, in order to speed up the convergence of optimization process, an element investment importance analysis is carried out to evaluate the importance of components to the lifeline network. Then a topology optimization model is established. The aim of the model is to find the least‐cost network topology while the seismic reliability between the sources and each terminal satisfies prescribed reliability constraints. For this optimization problem, a genetic algorithm, which takes network topologies as the individuals of its population, is used to search for the optimal solutions by suitable operators, including selection, crossover and mutation operators. The capacity of the proposed algorithm is illustrated by its applications to a simple example network consisting of 10 nodes and an actual network with 391 nodes located in a large city of China. Copyright © 2008 John Wiley & Sons, Ltd.     

基于混合遗传算法的斜测电离图参数反演

斜向探测是获取电离层状态信息的重要手段之一,对斜测电离图的反演可以得到电离层的相关结构参数.遗传算法是一种有效的并得到普遍应用的反演方法,该算法的求解不依赖于初值的选择,可以有效地减少反演问题解的非唯一性,但也存在“过早收敛”和局部搜索能力差等缺陷,从而导致反演精度下降,影响反演结果的可靠性.本文提出将基于模拟退火的混合遗传算法应用到斜测电离图的参数反演中,该算法不仅把握总体能力强,且具有较强的局部搜索能力,是遗传算法和模拟退火算法的优势互补.为了验证该算法反演结果的可靠性和稳定性,首先分别采用遗传算法、模拟退火算法和混合遗传算法对合成的电离图进行反演,反演参数包括临界频率,最大电子浓度和半厚度.通过对三种算法反演结果的对比,得出混合遗传算法的反演结果最接近真实值,需要的迭代次数也远远小于其他两种算法;通过改变种群大小和总迭代次数来判断参数值的改变对三种算法反演结果的影响,得出混合遗传算法有效地降低了参数的选取对反演结果的影响.然后用这三种反演算法对实测电离图进行反演,并将它们的反演结果与斜测链路中点的实际垂测数据进行比较,结果显示混合遗传算法84.62%的反演结果可以控制在误差范围之内,高于遗传算法（76.93%）和模拟退火算法（65.38%）.这些都表明了混合遗传算法的反演结果具有较强的可靠性,在反演的寻优能力和稳定性上要明显优于遗传算法和模拟退火算法,对实测电离层图的反演具有很强的借鉴意义和应用价值.     

一种全局优化算法:遗传算法-单纯形法

针对遗传算法搜索效不高和早熟的缺点，本文提出了将遗传算法和单纯形法组合的一种全局优化算法，并用七个典型检验函数验证了此方法的有效性。     

A comparative study of Monte Carlo simple genetic algorithm and noisy genetic algorithm for cost-effective sampling network design under uncertainty

This study evaluates and compares two methodologies, Monte Carlo simple genetic algorithm (MCSGA) and noisy genetic algorithm (NGA), for cost-effective sampling network design in the presence of uncertainties in the hydraulic conductivity (K) field. Both methodologies couple a genetic algorithm (GA) with a numerical flow and transport simulator and a global plume estimator to identify the optimal sampling network for contaminant plume monitoring. The MCSGA approach yields one optimal design each for a large number of realizations generated to represent the uncertain K-field. A composite design is developed on the basis of those potential monitoring wells that are most frequently selected by the individual designs for different K-field realizations. The NGA approach relies on a much smaller sample of K-field realizations and incorporates the average of objective functions associated with all K-field realizations directly into the GA operators, leading to a single optimal design. The efficacy of the MCSGA-based composite design and the NGA-based optimal design is assessed by applying them to 1000 realizations of the K-field and evaluating the relative errors of global mass and higher moments between the plume interpolated from a sampling network and that output by the transport model without any interpolation. For the synthetic application examined in this study, the optimal sampling network obtained using NGA achieves a potential cost savings of 45% while keeping the global mass and higher moment estimation errors comparable to those errors obtained using MCSGA. The results of this study indicate that NGA can be used as a useful surrogate of MCSGA for cost-effective sampling network design under uncertainty. Compared with MCSGA, NGA reduces the optimization runtime by a factor of 6.5.     

A recursive decomposition algorithm for network seismic reliability evaluation

A new probabilistic analytical approach to evaluate seismic system reliability of large lifeline systems is presented in this paper. The algorithm takes the shortest path from the source to the terminal of a node weight or edge weight network as decomposition policy, using the Boolean laws of set operation and probabilistic operation principal, a recursive decomposition process then could be constructed. For a general weight network, the modified Torrieri method (NTR/T method) is introduced to combine with the suggested algorithm. Therefore, the recursive decomposition algorithm may be applied to evaluate the seismic reliability of general lifeline systems. A series of case studies, including a practical district electric power network system and a large urban water supply system, show that the suggested algorithm supplies a useful probabilistic analysis means for the seismic reliability evaluation of large lifeline systems. Copyright © 2002 John Wiley & Sons, Ltd.     

Using a hybrid genetic algorithm–simulated annealing algorithm for fuzzy programming of reservoir operation

We present a novel approach for optimizing reservoir operation through fuzzy programming and a hybrid evolution algorithm, i.e. genetic algorithm (GA) with simulated annealing (SA). In the analysis, objectives and constraints of reservoir operation are transformed by fuzzy programming for searching the optimal degree of satisfaction. In the hybrid search procedure, the GA provides a global search and the SA algorithm provides local search. This approach was investigated to search the optimizing operation scheme of Shihmen Reservoir in Taiwan. Monthly inflow data for three years reflecting different hydrological conditions and a consecutive 10‐year period were used. Comparisons were made with the existing M‐5 reservoir operation rules. The results demonstrate that: (1) fuzzy programming could effectively formulate the reservoir operation scheme into degree of satisfaction α among the users and constraints; (2) the hybrid GA‐SA performed much better than the current M‐5 operating rules. Analysis also found the hybrid GA‐SA conducts parallel analyses that increase the probability of finding an optimal solution while reducing computation time for reservoir operation. Copyright © 2007 John Wiley & Sons, Ltd.     

正则化偏移成像的全局优化快速算法

目前,偏移后的地震剖面往往只是一个地质构造图像,还不能为后续的岩性分析和油气储层属性的提取提供更精确的信息.为了得到高分辨率真振幅的图像,建议采用正则化偏移成像方法.针对本问题数据规模大和正演算子矩阵稀疏的特点,提出采用一种新的算法--无记忆拟牛顿-模拟退火法对偏移算子方程进行求解.该方法综合了无记忆拟牛顿法优良的局部...     

Feasibility of waveform inversion of Rayleigh waves for shallow shear-wave velocity using a genetic algorithm

Conventional surface wave inversion for shallow shear (S)-wave velocity relies on the generation of dispersion curves of Rayleigh waves. This constrains the method to only laterally homogeneous (or very smooth laterally heterogeneous) earth models. Waveform inversion directly fits waveforms on seismograms, hence, does not have such a limitation. Waveforms of Rayleigh waves are highly related to S-wave velocities. By inverting the waveforms of Rayleigh waves on a near-surface seismogram, shallow S-wave velocities can be estimated for earth models with strong lateral heterogeneity. We employ genetic algorithm (GA) to perform waveform inversion of Rayleigh waves for S-wave velocities. The forward problem is solved by finite-difference modeling in the time domain. The model space is updated by generating offspring models using GA. Final solutions can be found through an iterative waveform-fitting scheme. Inversions based on synthetic records show that the S-wave velocities can be recovered successfully with errors no more than 10% for several typical near-surface earth models. For layered earth models, the proposed method can generate one-dimensional S-wave velocity profiles without the knowledge of initial models. For earth models containing lateral heterogeneity in which case conventional dispersion-curve-based inversion methods are challenging, it is feasible to produce high-resolution S-wave velocity sections by GA waveform inversion with appropriate priori information. The synthetic tests indicate that the GA waveform inversion of Rayleigh waves has the great potential for shallow S-wave velocity imaging with the existence of strong lateral heterogeneity.     

Stochastic analysis of ground response using non-recursive algorithm

During an earthquake, the amplitudes of seismic wave may amplify significantly as it propagates through the soil layers near the ground surface. Analysis of site amplification potential is strongly influenced by the uncertainty associated to the definition of soil thickness and its properties. In this paper, the non-recursive algorithm is used in linear and nonlinear Hybrid Frequency Time Domain (HFTD) approaches for stochastic analysis of site amplification. The non-recursive algorithm causes time reduction of analysis that is the essential base of stochastic analysis. The selected soil stochastic parameters are shear wave velocity, density, damping and thickness. The results of sensitivity analysis also show that the damping ratio is the most effective parameter in PGA at ground surface. The stochastic peak ground acceleration, response spectrum and amplification factor at the ground surface are determined by the two approaches for four sites with different average shear wave velocities. Comparison of the results shows that the nonlinear HFTD approach predicts closer response to real recorded data with respect to linear HFTD.     

An integrated decision method for prediction of tropical cyclone movement by using genetic algorithm

Typhoon disaster is one of the most influentialnatural disasters. The strong storm surge of Bangla-desh cyclone, which happened in November 1970,resulted in 300000 deaths. Asia and the western Pa-cific can be regarded as an area where is affected mostseverely by the disasters of tropical storms[1]. Themost important thing is to improve the accuracy oftyphoon tracks prediction internationally. Lande-sea,C.[2] pointed out that for a successful forecast oftropic cyclone (TC) tracks, it is not …     

实数编码遗传算法在大地电磁测深二维反演中的应用

作为全局非线性优化的新方法之一的遗传算法,近年来已从生物工程流行到大地电磁测深资料解释中.然而,大地电磁反演问题具有不适定性,解的非唯一性.通过结合求解不适定问题的Tikhonov正则化方法,本文采用实数编码遗传算法求解大地电磁二维反演问题.此算法在构建目标函数时引入正则化的思想,利用遗传算法求解最优化问题.常规的基于局部线性化的最优化反演方法易使解陷入局部极小值,而且严重的依赖初始模型的选择.与传统线性化的迭代反演方法相比,实数编码遗传算法能够克服传统方法的不足且能获得更好的反演结果.通过对大地电磁测深理论模型进行计算,结果表明:该算法具有收敛速度快、解的精度高和避免出现早熟等优点,可用于大地电磁资料解释.     

Forecasting streamflow by combination of a genetic input selection algorithm and wavelet transforms using ANFIS models

ABSTRACT

In this study, a data-driven streamflow forecasting model is developed, in which appropriate model inputs are selected using a binary genetic algorithm (GA). The process involves using a combination of a GA input selection method and two adaptive neuro-fuzzy inference systems (ANFIS): subtractive (Sub)-ANFIS and fuzzy C-means (FCM)-ANFIS. Moreover, the application of wavelet transforms coupled with these models is tested. Long-term data for the Lighvan and Ajichai basins in Iran are used to develop the models. The results indicate considerable improvements when GA selection and wavelet methods are used in both models. For example, the Nash-Sutcliffe efficiency (NSE) coefficient for Lighvan using FCM-ANFIS is 0.74. However, when GA selection is applied, the NSE is improved to 0.85. Moreover, when the wavelet method is added, the performance of new hybrid models shows noticeable enhancements. The NSE value of wavelet-FCM-ANFIS is improved to 0.97 for Lighvan basin.

Editor D. Koutsoyiannis Associate editor E. Toth     

Mallat算法在数字地震信号压缩中的应用

地震台站多、数据采集量大,日产出数据量庞大,研究数字地震信号的压缩方法成为行业热门课题。尝试将Mallat算法应用于数字地震波形数据压缩。选取不同的小波分解函数,对不同类型的数字地震信号进行3—5层的小波分解,将得到的小波系数进行分层硬阈值重构运算,对原始信号和处理信号进行压缩。分析可知,Mallat算法压缩比更高,与原始信号相比,重构信号不失真、能量保留系数高。     

震级加权四指标Blade算法及在地震带识别中的应用

Blade算法的产生是根据地震点的分布寻找地震断层，其优势在于能从较稀疏的点分布中找出线型条带模式，但不足的是完全不考虑地震的震级屑性。针对Blade算法的几个不足之处逐步改进，提出了震级加权四指标Blade算法。算法主要的改进体现在：①引入以震级为变量的权重函数，体现了高震级地震的控制作用，通过模拟与实例比较了三种权重函数的效果；②改进原方法中所挑出的Blade中心必须有地震点，引入四指标评价体系代替原方法的二指标评价体系，同时还根据指标值将挑选出的线型条带修正到更淮确的位置。将原算法与改进算法应用于华北地区4级以上的地震数据集以按寻地震带，结果表明，震级加权四指标Blade算法较Blade算法有显改进，取得了较好的应用效果。     

基于加权一致性的蚁群算法在断层检测中的应用

突出地震数据振幅空间不连续性的断层增强属性体是断层自动解释的基础,而如何压制噪声、地层残余响应、角度不整合接触等伪断层的影响是断层自动检测的关键问题之一.在指纹图像边缘检测处理中,一致性是对指示局部方向场信息各向异性强度的一个度量,本文将其作为一种新的断层增强属性引入到地震数据处理中.为了更有效地压制噪声,本文通过对一致性进行加权处理,再结合蚁群算法,提出了新的断层自动检测方法——基于加权一致性的蚁群算法.理论地震模型和实际地震数据的处理结果表明,对于相同的断层检测过程,对比常规的基于方差属性的蚁群算法和基于C3相干属性的蚁群算法,本文所提方法不仅更有效地压制了噪声和地层残余响应产生的伪断层信息,而且更完整地展现了断层的延伸长度,同时具有更高的稳定性和计算效率.     

双稀疏字典和FISTA的地震数据去噪

地震数据的随机噪声去除是地震数据处理中的一项重要步骤,双稀疏字典提供了两层稀疏模型,比单层稀疏模型可以更好地去除噪声.该方法首先利用contourlet变换对地震数据进行稀疏表示,然后在contourlet域中使用快速迭代收缩阈值算法（fast iterative shrinkage-thresholding algorithm,FISTA）对初始字典系数进行更新,接着采用数据驱动紧标架（data-driven tight frame,DDTF）在contourlet域中得到DDTF字典并通过FISTA得到更新后的字典系数,最后通过DDTF字典和更新后的字典系数获得新的contourlet系数,并对新的contourlet系数进行硬阈值和contourlet反变换得到去噪后的数据.通过模拟数据和实际数据的实验证明：与固定基变换去噪方法相比,该方法可以自适应地对地震数据进行稀疏表示,在地震数据较为复杂时得到更高的信噪比;与字典学习去噪方法相比,该方法不仅拥有较快的去噪速度,而且克服了字典学习因为缺少先验约束造成瑕疵的缺点.     

Steim2压缩算法的优点和缺点

基于SEED卷格式的Steim2压缩算法, 是目前国际国内的地震数据传输和本地存储中普遍采用的压缩算法, 该算法优点突出, 缺点明显. 其优点是:摸准了地震事件"长期平静"和"长期无震"的特点, 是专门符合该特点量身定做的数据压缩算法, 有显著的独到之处. 其缺点是:在地震事件"活跃期"或"突发期", 即对数据压缩需求...