利用应力加载速率变化与地震活动性模型探讨2013年吉林前郭震群成因的力学机制     

尚园程  史保平 《地球物理学报》2022,65(1):26-36

理解震群的成因机制是地震科学的重要挑战.本文基于速率-状态摩擦定律,利用Dieterich断层失稳模型探讨了流体运移、慢滑移和震群触发三者之间的关系,研究结果表明:(1)断层内部或周边流体流动所导致的孔隙压力升高可诱发断层发生慢滑或非地震滑移,慢滑或非地震滑移可使区域内临近断层上库仑应力加载速率升高,进而导致震群产生;...  相似文献   

一种改进的人工鱼群聚类算法     

李小培  张洪伟  邹书蓉 《成都信息工程学院学报》2014,29(5):485-490

为了克服K-Means算法对初始类簇中心、噪声点、孤立点敏感缺点,将K-Means算法和人工鱼群算法结合,提出了改进的人工鱼群聚类算法。在该算法中将类簇中心看作一条人工鱼,让每条人工鱼执行随机、觅食、聚群、追尾行为中的一种,并将更新后的位置作为K-Means算法的初始值,不断重复人工鱼的位置更新和K-Means操作,直到算法结束。由于在算法中加入了动态移动步长和全局人最优人工鱼位置,聚类的收敛精度和速度都得到提高。使用iris和glass数据集进行聚类时,与其他算法相比,文中的收敛时间缩短2.6%,精度提高1.36%。  相似文献   

基于粒子群优化BP神经网络的耕地自然质量分计算模型   总被引：1，自引：0，他引：1  

潘润秋  马小淞  刘珺 《地理与地理信息科学》2014,30(5)

针对常规农用地分等方法中因子权重计算存在人为因素干扰及BP神经网络模型自身优化过程中易陷入局部极小值的情况,通过粒子群算法的全局搜索能力对BP神经网络进行优化,建立耕地自然质量分计算的粒子群BP神经网络模型(PSO-BP网络模型)。对湖北省荆门市沙洋县进行实证研究,结果证明PSO-BP网络模型能避免常规方法因子权重确定的人为干扰,同时具有较高的优化效率,应用效果较好。  相似文献   

Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization     

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   

Groundwater-level forecasting under climate change scenarios using an artificial neural network trained with particle swarm optimization     

Evdokia Tapoglou  Ioannis C. Trichakis  Zoi Dokou  Ioannis K. Nikolos 《水文科学杂志》2013,58(6):1225-1239

Abstract

Artificial neural networks (ANNs) have recently been used to predict the hydraulic head in well locations. In the present work, the particle swarm optimization (PSO) algorithm was used to train a feed-forward multi-layer ANN for the simulation of hydraulic head change at an observation well in the region of Agia, Chania, Greece. Three variants of the PSO algorithm were considered, the classic one with inertia weight improvement, PSO with time varying acceleration coefficients (PSO-TVAC) and global best PSO (GLBest-PSO). The best performance was achieved by GLBest-PSO when implemented using field data from the region of interest, providing improved training results compared to the back-propagation training algorithm. The trained ANN was subsequently used for mid-term prediction of the hydraulic head, as well as for the study of three climate change scenarios. Data time series were created using a stochastic weather generator, and the scenarios were examined for the period 2010–2020.