动态遗传算法及其在P波初动震源机制解中的应用

利用动态遗传算法对Ｐ波初动震源机制解进行反演计算，并与前人常用的反演计算方法对比，认为遗传算法在Ｐ波初动 震源机制解中的应用是有很有效的。     

Generating basis sets of double differences

When a collection of double differences is used to compute global-positioning-system satellite orbits from a permanent network of receiving stations, linear dependence among the double-differenced observations reduces the number of double differences that contribute new information to the computations. A maximal linearly independent subset of a large collection of double differences contains all the information content of the full set. If r is the number of receivers and s is the number of satellites, the original collection of double differences may have size O(r ² s ²), whereas the linearly independent subset has size no greater than O(rs). Only such a smaller independent subset needs to participate in the more expensive double-precision matrix computations to correctly correlate all double differences, detect cycle slips, resolve ambiguities, and compute satellite orbits and station positions and relative velocities. Dependence among double differences is characterized using vector space methods together with geometric characterizations of Boolean matrices. These characterizations lend themselves to fast, robust algorithms for computing maximal linearly independent sets (bases) of double differences. An algorithm is given for constructing a generating independent set of double differences from the Boolean array of receiving-station/satellite connections. Characterizations of generator equivalence allow alternative generating sets to be identified and selected. An updating algorithm to handle local changes in the satellite–receiver connection matrix is also described. Received: 27 August 1996 / Accepted: 28 January 1999     

基于神经网络和滤波技术的贮仓结构参数识别

利用神经网络和Kalman滤波技术,提出了一种直接识别结构物理参数的方法,用Kalman滤波技术训练网络。在贮仓振动台实验的基础上,用贮仓在动载作用下的位移、速度作为网络的输入,激振加速度和响应加速度作为网络的输出。仿真计算表明,本文方法是可行的。     

On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks

ABSTRACT

Artificial neural networks (ANNs) become widely used for runoff forecasting in numerous studies. Usually classical gradient-based methods are applied in ANN training and a single ANN model is used. To improve the modelling performance, in some papers ensemble aggregation approaches are used whilst in others, novel training methods are proposed. In this study, the usefulness of both concepts is analysed. First, the applicability of a large number of population-based metaheuristics to ANN training for runoff forecasting is tested on data collected from four catchments, namely upper Annapolis (Nova Scotia, Canada), Biala Tarnowska (Poland), upper Allier (France) and Axe Creek (Victoria, Australia). Then, the importance of the search for novel training methods is compared with the importance of the use of a very simple ANN ensemble aggregation approach. It is shown that although some metaheuristics may slightly outperform the classical gradient-based Levenberg-Marquardt algorithm for a specific catchment, none performs better for the majority of the tested ones. One may also point out a few metaheuristics that do not suit ANN training at all. On the other hand, application of even the simplest ensemble aggregation approach clearly improves the results when the ensemble members are trained by any suitable algorithms.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR E. Toth     

A spatial approach to forest‐management optimization: linking GIS and multiple objective genetic algorithms

Forest‐management decision‐support systems are largely monolithic structures. Spatial details are left out during the optimization process and are elaborated during the operational planning. This might produce misleading results and plans that are impossible to implement. In this paper, a forest‐management spatial decision‐support systems is presented, in which spatial formulation needed for wildlife models is included during the optimization process. To this end, a multiple‐objective genetic algorithm is combined with a geographical information system. An online spatial evaluation of the objective functions is made possible. This is illustrated by a pilot study in Kirkhill forest, Aberdeen.     

一种启发式A*算法和网格划分的空间可达性计算方法

本文提出了一个适用于研究城市内部的个体或商业区位的微观可达性计算方法,该方法的核心是将研究区域进行等距的网格划分,通过计算每个网格的可达性指标,来研究整个区域的可达性的空间分布特点。在可达性计算中,利用网格内的道路密度和土地利用状态这两个因素来模拟计算每个网格的交通成本,引入了启发式A ^*空间搜索算法来计算网格间路径的交通成本,并且加入适当的启发信息,提高了搜索效率,使搜索结果更符合实际需求。最后,基于本文提出的方法,利用GIS二次开发工具ArcEngine开发了计算程序,收集了多源数据,以广州市商务区的可达性作为计算对象,进行了商务区的可达性和易达性案例计算。     

有限断层模型在2015年9月16日智利Mw8.3级地震海啸数值模拟中的应用与评估

2015年9月16日22时54分（当地时间）智利中部近岸发生M_w8.3级地震,震源深度25 km。同时,强震的破裂区长200 km,宽100 km,随之产生了中等强度的越洋海啸。海啸影响了智利沿岸近700 km的区域,局部地区监测到近5 m的海啸波幅和超过13 m的海啸爬坡高度。太平洋区域的40多个海啸浮标及200多个近岸潮位观测站详细记录了此次海啸的越洋传播过程,为详细研究此次海啸近场及远场传播及演化规律提供了珍贵的数据。本文选择有限断层模型和自适应网格海啸数值模型建立了既可以兼顾越洋海啸的计算效率又可以实现近场海啸精细化模拟的高分辨率海啸模型。模拟对比分析了海啸的越洋传播特征,结果表明采用所建立的模型可以较好地再现远场及近场海啸特征,特别是对近场海啸的模拟结果非常理想。表明有限断层可以较好地约束近场、特别是局部区域的破裂特征,可为海啸预警提供更加精确的震源信息,结合高分辨率的海啸数值预报模式实现海啸传播特征的精细化预报。本文结合观测数据与数值模拟结果初步分析了海啸波的频散特征及其对模型结果的影响。同时对观测中典型的海啸波特征进行的简要的总结。谱分析结果表明海啸波的能量主要分布在10~50 min周期域内。这些波特征提取是现行海啸预警信息中未涉及,但又十分重要的预警参数。进一步对这些波动特征的详细研究将为海啸预警信息及预警产品的完善提供技术支撑。     

Bayesian inversion with Markov chains—I. The magnetotelluricone-dimensional case

We use Monte Carlo Markov chains to solve the Bayesian MT inverse problem in layered situations. The domain under study is divided into homogeneous layers, and the model parameters are the conductivity of each layer. We use an a priori distribution of the parameters which favours smooth models. For each layer, the a priori and a posteriori distributions are digitized over a limited set of conductivity values.
  The Markov chain relies on updating the model parameters during successive scanning of the domain under study. For each step of the scanning, the conductivity is updated in one layer given the actual value of the conductivity in the other layers. Thus we designed an ergodic Markov chain, the invariant distribution of which is the a posteriori distribution of the parameters, provided the forward problem is completely solved at each step.
  We have estimated the a posteriori marginal probability distributions from the simulated successive values of the Markov chain. In addition, we give examples of complex magnetotelluric impedance inversion in tabular situations, for both synthetic models and field situations, and discuss the influence of the smoothing parameter.     

大型地下洞室群参数反演及其工程应用

基于三维弹塑性有限元及遗传算法,结合西龙池抽水蓄能电站工程实际,首先用小模型分段模拟工程中有明显差异的几个不同段位,并反演得到围岩物理力学参数及支护前围岩位移释放系数。在此基础上,对洞室进行了二期开挖计算,成功预测了二期开挖后洞室变位、围岩稳定、锚杆锚索应力等状况,揭示了洞室应力集中和围岩的潜在破坏部位,对工程施工具有重要指导意义。     

Human genome behaviour: a powerful mechanism for optimizing the use of space technology in surveying networks design

An approach based on the behaviour of human genome is developed to efficiently provide a general framework for optimizing the use of space technology in surveying networks design. The developed approach attempts to apply the successful self-organizing principles based upon the biological evolution to artificial intelligence. It mimics the phenomena of natural selection observed in nature to achieve its goals by continuously adopting a population of candidate solutions and improving its performance over successive generations. The goal of adaptation is to find the best solution that optimizes the design of a surveying network based on the use of satellite observations. This network can be defined as a set of stations, co-ordinated by a series of sessions formed by placing receivers on the stations. The problem is to search for the best order for observing these sessions to give the best observation schedule at minimum cost. The obtained results prove the effectiveness of the developed technique in term of solution quality and computational efforts. The Corresponding Author in formed me that his address is likely to change soon. Please contact me, leick@spatial.maine.edu for the latest on this change.