共查询到18条相似文献，搜索用时 390 毫秒
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瞬变电磁圆锥型场源装置有效的减小了线圈间的电感,提高了小装置探测地下浅层的分辨率,但常规反演方法需给定初始模型且反演精度不高.针对瞬变电磁法反演计算问题,通过对粒子群优化算法(PSO)和神经网络算法(BP)分析研究,改进了一种基于神经网络算法Sigmoid函数的自适应加权粒子群优化(AWPSO)算法.采用标准测试函数对算法进行试算,建立多个理论层状地质模型对该算法进行理论验证,最后在地质资料已知地区开展现场实验.计算结果表明,新提出的算法具有更高的全局搜索寻优能力和收敛速度快、计算精度高,且不需要给定初始模型;实验结果显示实测数据反演结果与高密度电法探测结果吻合,证明该算法能够对瞬变电磁探测数据进行反演计算且精度较高,可以在同类型的浅层探测任务中提供参考. 相似文献
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在地震综合预测投影寻踪研究工作中,投影寻踪回归算法是其中应用最多的一种方法．但一般投影寻踪回归算法构造技术较为复杂,采用多次局部光滑回归,计算量较大,外推较为繁杂,容易陷于局部解．在综合考虑传统投影寻踪回归算法特点的基础上,针对投影寻踪回归计算中存在的一些不利因素,给出了一定的解决思路:采用粒子群优化算法代替高斯 牛顿算法优化投影方向;采用厄米多项式代替分段线性光滑回归来拟合岭函数,以简化优化过程;参数优化无需分组,获得全局优化的岭函数．利用数值仿真技术进行基于粒子群优化算法与厄米多项式构建的投影寻踪回归模型建模能力与计算精度的检验,再将其应用于多维地震时间序列和一般多维无序地震样本回归综合建模预测中．通过计算和分析表明,基于粒子群优化算法与厄米多项式构建的投影寻踪回归模型具有简单、快速、有效的特点,在实际地震综合预测建模中取得了满意的效果,可作为地震预测的一种综合分析方法． 相似文献
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磁法反演属于非线性最优化问题,具有多变量、目标函数多极值、反演多解性等特点,因此,需要稳定的和高效的优化反演算法.粒子群优化已开始被用于地球物理反演计算,但是对于高维数、多峰值函数,粒子群的收敛精度不高,容易陷入局部极值.如果将混沌局部搜索和粒子群优化的优势相结合,通过将种群搜索过程对应为混沌轨道的遍历过程,可使标准粒子群优化的搜索过程具有避免陷入局部极小的能力.本文利用混沌-粒子群优化用于磁法反演计算.数据试验结果表明,该方法可以用于磁法数据的地球物理非线性反演,并且在一定程度上优于标准粒子群优化方法. 相似文献
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瑞雷波频散曲线反演的本质是对目标函数求极值的过程.传统的线性局部反演算法容易陷入局部极小值,增加了反演结果的不确定性.粒子群算法作为一种全局非线性优化手段,能够保证各搜索空间内局部寻优的同时,逼近全局最优,保证迭代反演的收敛性.建立含软夹层型地层模型,正演计算得到理论频散曲线,采用粒子群算法反演得到的横波速度与模型高度吻合.同时,在理论频散曲线中加入10%的高斯白噪声,粒子群算法的反演结果仍然具有很好的可靠程度.通过对实测频散曲线的反演表明:粒子群算法在含软夹层型地层的横波速度探测中相对于最小二乘法优势明显,具有很高的应用价值. 相似文献
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《地震工程与工程振动》2021,41(2)
粒子群优化算法是模拟群体智能所建立起来的一种全局优化算法,在解决多参数非线性函数的优化问题上具有良好的性能,为了有更好的收敛精度和更快的收敛速度,本文构建了带有压缩因子的粒子群算法,可用于设计反应谱的标定。利用这一方法可给出第一拐点周期、特征周期、平台值和衰减指数等刻画设计反应谱特征的参数值。本文以埃尔森特罗台(El Centro)加速度时程的反应谱标定为例,采用本文提出的改进粒子群算法、Newmark三参数法、双参数法和差分进化算法对其进行标定。对比分析了4种标定方法给出的特征参数及计算精度,实例证明,改进粒子群算法具有较高的精度,给出的设计反应谱较好地反映了地震反应谱的特征。 相似文献
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重力梯度数据相对于传统重力数据,能够更细致、准确地描述地球浅部构造和研究矿产资源分布等信息.本文采用共轭梯度算法,在加权密度域求解重力梯度数据三维聚焦反演最优化问题,以恢复地下三维密度分布,目标函数包括数据不拟合函数和最小支撑稳定函数.首先,在推导目标函数对加权密度的一阶导数时,为了得到更合理的计算公式,我们考虑变加权函数中含有密度变量;此外,本文通过密度上下限约束,改善了传统聚焦反演中聚焦因子选取困难的问题.新算法获得的反演结果,对聚焦因子的选择约束较少,相比传统聚焦算法,能够更容易的获得理想结果.将方法应用于理论模型验证其有效性和正确性,并应用本文方法处理文顿盐丘地区的航空全张量重力梯度数据,得到了与已知地质信息匹配的密度分布,表明本文方法具有处理实际数据的能力. 相似文献
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煤矿井下矿井瞬变电磁法(MTEM)探测中,电磁场呈全空间分布,全空间瞬变电磁反演是复杂的非线性问题,目前反演计算中全空间响应主要由半空间响应乘以全空间响应系数来得到,导致反演结果中顶板和底板异常(或前方和后方异常)叠加在一起难以分离,造成分辨率下降.论文提出采用粒子群优化算法(PSO)进行全空间MTEM反演,通过理论分析,在常规的粒子群算法基础上提出了一种新的进化公式改进策略,提高了粒子群算法的寻优能力.基于全空间瞬变电磁场理论,编写了粒子群算法反演程序,进行全空间条件下五层含巷道的复杂模型的反演计算.结合某矿井巷道顶板、底板岩层及断层含水性的探测实例,对实测数据进行反演计算和解释,探测结果得到钻探证实.研究表明,改进的粒子群优化算法对理论模型和实际资料的反演拟合程度较高,实现了矿井顶板、底板视电阻率异常的分离,提高了全空间瞬变电磁勘探资料的解释精度和分辨率. 相似文献
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当前对建筑空间结构进行优化时,所采用的算法趋同性高,无法实现多目标种群优化,易陷入局部最优解,存在寻优质量低、优化成本高、抗震性能低的问题。针对上述问题,提出一种基于改进粒子群算法的建筑空间结构优化方法。该方法以空间结构的抗震性能、工程造价为优化目标,来优化建立建筑空间结构设计;引入多子群协同进化机制解决建筑空间结构抗震优化设计中多目标间的种群优化问题,同时引入外部档案和精英学习策略改进粒子群算法,筛选出满足目标函数的最优设计方案,完成抗震性约束的建筑空间结构优化。实验结果表明:所提方法对建筑空间结构优化时的特点为寻优质量高、优化成本低、抗震性能高。 相似文献
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Rubén Fernández-Casal Mario Francisco-Fernández 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(5):1247-1259
In geostatistics, the approximation of the spatial dependence structure of a process, through the estimation of the variogram or the covariogram of the variable under consideration, is an important issue. In this work, under a general spatial model, including a mean or trend function, and without assuming any parametric model for this function and for the dependence structure of the process, a general nonparametric estimator of the variogram is proposed. The new approach consists in applying an iterative algorithm, using the residuals obtained from a nonparametric local linear estimation of the trend function, jointly with a correction of the bias due to the use of these residuals. A simulation study checks the validity of the presented approaches in practice. The broad applicability of the procedures is demonstrated on a real data set. 相似文献
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Jörg Benndorf Joachim Menz 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(3):627-637
Modern methods of geostatistics deliver an essential contribution to Environmental Impact Assessment (EIA). These methods allow for spatial interpolation, forecast and risk assessment of expected impact during and after mining projects by integrating different sources of data and information. Geostatistical estimation and simulation algorithms are designed to provide both, a most likely forecast as well as information about the accuracy of the prediction. The representativeness of these measures depends strongly on the quality of the inferred model parameters, which are mainly defined by the parameters of the variogram or the covariance function. Available data may be sparse, trend affected and of different data type making the inference of representative geostatistical model parameters difficult. This contribution introduces a new method for best fitting of the geostatistical model parameters in the presence of a trend, which utilizes the empirical and theoretical differences between Universal Kriging and trend-predictions. The method extends well known approaches of cross validation in two aspects. Firstly, the model evaluation is not only limited to sample data locations but is performed on any prediction locations of the attribute in the domain. Secondly, it extends the measure used in cross validation, based on a single point replacement by using error curves. These allow defining rings of influence representing errors resulting from separate variogram lags. By analyzing the different variogram lags the fit of the complete covariance can be assessed and the influence of the several model parameters separated. The use of the proposed method in an EIA context is illustrated in a case study related on the prediction of mining-induced ground movements. 相似文献
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Mohammad Ali Ahmadi Sohrab Zendehboudi Ali Lohi Ali Elkamel Ioannis Chatzis 《Geophysical Prospecting》2013,61(3):582-598
Reservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Permeability is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, a soft sensor on the basis of a feed‐forward artificial neural network was implemented to forecast permeability of a reservoir. Then, optimization of the neural network‐based soft sensor was performed using a hybrid genetic algorithm and particle swarm optimization method. The proposed genetic method was used for initial weighting of the parameters in the neural network. The developed methodology was examined using real field data. Results from the hybrid method‐based soft sensor were compared with the results obtained from the conventional artificial neural network. A good agreement between the results was observed, which demonstrates the usefulness of the developed hybrid genetic algorithm and particle swarm optimization in prediction of reservoir permeability. 相似文献
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随机地震反演关键参数优选和效果分析(英文) 总被引：2，自引：0，他引：2
随机地震反演技术是将地质统计理论和地震反演相结合的反演方法,它将地震资料、测井资料和地质统计学信息融合为地下模型的后验概率分布,利用马尔科夫链蒙特卡洛(MCMC)方法对该后验概率分布采样,通过综合分析多个采样结果来研究后验概率分布的性质,进而认识地下情况。本文首先介绍了随机地震反演的原理,然后对影响随机地震反演效果的四个关键参数,即地震资料信噪比、变差函数、后验概率分布的样本个数和井网密度进行分析并给出其优化原则。资料分析表明地震资料信噪比控制地震资料和地质统计规律对反演结果的约束程度,变差函数影响反演结果的平滑程度,后验概率分布的样本个数决定样本统计特征的可靠性,而参与反演的井网密度则影响反演的不确定性。最后通过对比试验工区随机地震反演和基于模型的确定性地震反演结果,指出随机地震反演可以给出更符合地下实际情况的模型。 相似文献
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Anxu Wu Xiangdong Lin Changsheng Jiang Yongxian Zhang Xiaodong Zhang Mingxiao Li Ping&# an Li 《地震科学（英文版）》2009,22(5):563-574
In the research of projection pursuit for seismic comprehensive forecast, the algorithm of projection pursuit regression (PPR) is one of most applicable methods. But generally, the algorithm structure of the PPR is very complicated. By partial smooth regressions for many times, it has a large amount of calculation and complicated extrapolation, so it is easily trapped in partial solution. On the basis of the algorithm features of the PPR method, some solutions are given as below to aim at some shortcomings in the PPR calculation: to optimize project direction by using particle swarm optimization instead of Gauss-Newton algorithm, to simplify the optimal process with fitting ridge function by using Hermitian polynomial instead of piecewise linear regression. The overall optimal ridge function can be obtained without grouping the parameter optimization. The modeling capability and calculating accuracy of projection pursuit method are tested by means of numerical emulation technique on the basis of particle swarm optimization and Hermitian polynomial, and then applied to the seismic comprehensive forecasting models of poly-dimensional seismic time series and general disorder seismic samples. The calculation and analysis show that the projection pursuit model in this paper is characterized by simplicity, celerity and effectiveness. And this model is approved to have satisfactory effects in the real seismic comprehensive forecasting, which can be regarded as a comprehensive analysis method in seismic comprehensive forecast. 相似文献