普陀山植被对旅游干扰的响应研究

应用有序样方聚类法,把普陀山旅游区32个样方分为5个群落类型,分别是马尾松-青冈+檵木群丛、马尾松-青冈+檵木+红楠+窄基红褐柃群丛、马尾松+枫香+檵木-青冈+檵木群丛、马尾松+青冈-青冈+檵木群丛、马尾松+青冈-青冈+檵木+石栎群丛,并运用TW INSPAN对上述样方进行聚类分析。两种分类方法的结果具有极显著的关联,说明有序样方聚类法很好地揭示了旅游干扰和群落类型之间的关系,它更适合于旅游干扰分析中的聚类研究,在旅游干扰的研究中是一个有益且有效的尝试。     

改进的最优窗宽理论对年径流量的插值预测研究——以黄河利津等站为例

针对小样本数据造成年径流量预测效果不理想的情况,以及非对称和非正态资料的处理问题,引入信息扩散和模糊映射思想,同时运用遗传算法改进最优窗宽理论,建立了新的扩散插值模型。该模型通过对零散数据点的信息进行模糊扩散,进而实现对有限数据点信息向其邻近区域点的概率插值。选取黄河利津站为例,根据其近70 a(1942—2011年)径流量实测数据,进行了缺损数据的插值和预测试验,同时与正态扩散插值模型进行对比分析,结果表明:1)预测值能较好地模拟实际径流序列的波形变化,对丰水年(如2007年)和枯水年(如2009年)的预报都比较准确;2)中长期预报(10a)平均相对误差仅为11.59%,相较传统方法有较大的改进;3)以黄河流域2个站点(花园口和兰州)和长江流域的3个站点(朱沱、宜昌和大通)年径流量预测试验以及海温资料的插值试验作为补充,验证了该算法的有效性和普适性。该模型可为实际水文数据资料的客观分析和中长期预报提供参考。     

资水流域柘桃区间中小河流域面雨量算法比较分析

为探析国家气象信息中心多源融合格点降水实况产品在中小河流面雨量计算中的可行性,该文以湖南省资水流域柘桃区间为例,利用泰森多边形法、算术平均值法、克里金插值法以及国家气象信息中心多源融合格点降水实况产品,以2020年9月的主要降水过程为代表,对4种方法计算的面雨量结果进行比较分析。结果表明：(1)4种方法的计算结果相关性显著,均能较好反应出不同强度降水及不同子流域之间的差异。大多数情况下算术平均值法的结果偏大,多源融合格点实况法的结果偏小,插值法和泰森多边形法的结果介于二者之间。(2)当地面雨量站分布密集或降水空间分布均匀时,4种方法计算结果的标准差较小;当地面雨量站分布稀疏或降水空间分布不均时,4种方法的结果标准差较大,此时采用多源融合格点实况法比算术平均值法、泰森多边形法更合理可靠。(3)对于地形地貌复杂多变的山区,尤其是暴雨以上强降水天气,降水空间分布十分不均,实际计算中小河流域面雨量时,针对不同的子流域、不同的天气过程,应当根据天气系统及降水空间分布综合分析,合理利用多种方法才能准确把握面雨量的大小和可能的误差。     

Single‐and multi‐objective genetic algorithm optimization for identifying soil parameters

This paper discusses the quality of the procedure employed in identifying soil parameters by inverse analysis. This procedure includes a FEM‐simulation for which two constitutive models—a linear elastic perfectly plastic Mohr–Coulomb model and a strain‐hardening elasto‐plastic model—are successively considered. Two kinds of optimization algorithms have been used: a deterministic simplex method and a stochastic genetic method. The soil data come from the results of two pressuremeter tests, complemented by triaxial and resonant column testing. First, the inverse analysis has been performed separately on each pressuremeter test. The genetic method presents the advantage of providing a collection of satisfactory solutions, among which a geotechnical engineer has to choose the optimal one based on his scientific background and/or additional analyses based on further experimental test results. This advantage is enhanced when all the constitutive parameters sensitive to the considered problem have to be identified without restrictions in the search space. Second, the experimental values of the two pressuremeter tests have been processed simultaneously, so that the inverse analysis becomes a multi‐objective optimization problem. The genetic method allows the user to choose the most suitable parameter set according to the Pareto frontier and to guarantee the coherence between the tests. The sets of optimized parameters obtained from inverse analyses are then used to calculate the response of a spread footing, which is part of a predictive benchmark. The numerical results with respect to both the constitutive models and the inverse analysis procedure are discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

Genetic algorithms in probabilistic finite element analysis of geotechnical problems

In application to numerical analysis of geotechnical problems, the limit-state surface is usually not known in any closed form. The probability of failure can be assessed via the so-called reliability index. A minimization problem can naturally be formed with an implicit equality constraint defined as the limit-state function and optimization methods can be used for such problems. In this paper, a genetic algorithm is proposed and incorporated into a displacement finite element method to find the Hasofer–Lind reliability index. The probabilistic finite element method is then used to analyse the reliability of classical geotechnical systems. The performance of the genetic algorithm (GA) is compared with simpler probability methods such as the first-order-second-moment Taylor series method. The comparison shows that the GA can produce the results fairly quickly and is applicable to evaluation of the failure performance of geotechnical problems involving a large number of decision variables.     

一种CMP道集剩余校正量求取算法

地震资料处理中CMP道集在经过静校正和动校正处理之后,仍然会存在剩余校正量。剩余校正量的存在影响叠加成像效果。传统剩余校正量求取采用基于统计规律的互相关算法。设计了一种振幅绝对误差剩余校正量求取算法,并用MATLAB编程进行了仿真测试和实际地震资料处理试验。两种算法有着相同的效果,但振幅绝对误差算法速度更快。     

物化探彩色成图非线性编码技术

本文总结了物化探数据彩色成图技术过程，把非线性科学技术引入物化探数据彩色成图编码。其包括三个方面的内容：指出目前物化探彩色成图编码方法的一些缺点；ＳＯＦＭ算法编码技术的实现；指出物化探数据彩色成图编码是一个最优化问题，构造出这个问题的模型后，应用遗传算法求解模型，进行成图编码。     

Adaptive estimation of wave parameters by Geno-Kalman filtering

Prediction of wave parameters is very important for planning, designing and operation of ocean structures. Accurate estimation of these parameters provides engineers to construct more economical and reliable ocean structures such as harbors, breakwaters, oil production platforms and ocean wave energy converters. For this reason, optimum operation of these plants has become a must. Various methods have been introduced to determine the relation among wind speed previous and current wave parameters. Method proposed in this paper consists of genetic algorithms and Kalman filters which is called as Geno-Kalman filtering. It is based on adaptive calculation to reach the solution. Also a comparison has been made between perceptron Kalman filtering and Geno-Kalman filtering techniques. The application of Geno-Kalman filtering was performed for station 46002 which located in the Coos Bay at Oregon, USA. It is observed that the Geno-Kalman filtering methodology has smaller absolute, mean-square and relative errors than perceptron Kalman filtering. Also coefficient of efficiency value which was used to evaluate results between observed and estimated is higher at Geno-Kalman filtering than perceptron Kalman filtering.     

基于阵列感应测井的支持向量机流体识别方法

基于研究区块的高分辨率阵列感应测井(HDIL)资料,首先分析了利用交会图法进行流体识别的效果,指出了该常规解释方法的局限性。在此基础上,将基于遗传算法优化的支持向量机(GA-SVM)引入到测井解释当中,结合阵列感应测井和常规测井资料,建立了流体识别的非线性模型。利用该模型对45个训练样本进行回判,准确率为100%,对21个预测样本进行预测的准确率为90.48%,相对于常规解释方法,该流体识别方法具有更高的准确率。