软土大变形固结计算中参数识别的遗传算法

含水量很高的饱和软土在外荷载作用下沉降很大,小变形分析误差太大,必须通过大变形非线性固结计算来模拟。遗传算法是一种全局优化和搜索的仿生算法。近年来随着工程领域中复杂的大规模非线性系统的出现,遗传算法日益得到青睐,目前已经广泛应用到各个领域中。本文对遗传算法做了改进,主要体现在杂交算子的选取和轮盘赌模型的模拟退火拉伸,并将其用于饱和软土的大变形固结分析,解决其中的关键参数识别问题。研究表明该方法是行之有效的,值得进一步研究探索。     

基于遗传算法的线要素自动化简模型

线划要素是地图上大量存在的最基本的地图要素，作为自动综合的一个重要方面，它的化简受到了极大的关注。在分析总结现有线划要素化简方法的基础上，依据遗传算法的基本原理和方法，建立了地图上线划要素的化简模型，并讨论了基于遗传算法的线要素自动化简方法中的关键问题，给出了具体的操作步骤和实验结果。结果表明，利用遗传算法对线要素进行化简能够比较好地保持线要素的整体形状，同时还对点具有很高的压缩率。     

An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.     

Structuring velocity model of microseism based on genetic algorithm and Levenberg-Marquardt algorithm

为在射孔时间未知条件下达到微震速度模型矫正的目的,采用遗传算法交叉互换搜索最优初值和 Levenberg-Marquardt 算法局部锁定最优解的方式来构建微震速度模型。通过 6 层层状模型及井下不同数量检波器进行试算,随着检波器数量增加,构建的速度模型准确度越高,当检波器数量增加到 20 个时,误差约 13. 4 ×10 －3 ,目的层检波器数量越多,构建目的层速度误差越小,第 5 层检波器由 1 增加到 3 个时,精度提高 9. 1 ×10 －3 ,并且反演震源与实际震源距离相差 10 m,说明此方法在射孔时间未知条件下可以良好构建速度模型。     

云南省孟连西7.3级地震前跨断层形变反映

云南省孟连西７．３级地震前跨断层形变反映张兴华，宋金玲（云南省地震工程研究所，昆明６５０２０４）１９９５年７月１０日、１２日在滇西南孟连以西（北纬２２．１°东经９９．３°）连续发生Ｍｓ６．２级和７．３级强烈地震．１９９３年１月─１９９５年５月，云南中...     

Inversion of coseismic stress-triggered fault slips using borehole strainmeter observations

Coseismic stress-triggering is becoming a new hot spot of research. Coseismic strain steps recorded by borehole strainmeters are particularly valuable in studying coseismic stress-triggered fault slips. Based on the theory of dis location, one can invert the triggered fault slips with such data if he/she has a well understanding about the local faults. Genetic algorithm can be applied to significantly raise the efficiency of searching a best solution among all possibilities in this kind of inversion. A testifying check of the program and analyses of each parameter＇s influence may further enhance the reliability of inversion results. Taking complexity of geological structure into account, the inversion results should be regarded as the predominant property or a comprehensive effect of triggered local faults＇ activities. As an attempt, we inverted the assumingly active faults＇ slips triggered by the Ms=8.1 Kunlun Mountain earthquake over Beijing area.     

结合遗传算法的LVQ神经网络在声学底质分类中的应用

学习向量量化（Learning Vector Quantization，LVQ）神经网络在声学底质分类中具有广泛应用. 常用的LVQ神经网络存在神经元未被充分利用以及算法对初值敏感的问题，影响底质分类精度. 本文提出采用遗传算法（Genetic Algorithms，GA）优化神经网络的初始值，将GA与LVQ神经网络结合起来，迅速得到最佳的神经网络初始权值向量，实现对海底基岩、砾石、砂、细砂以及泥等底质类型的快速、准确识别. 将其应用于青岛胶州湾海区底质分类识别研究中，通过与标准的LVQ神经网络的分类结果进行比较表明，该方法在分类速度以及精度上都有了较大提高.     

基于WOA-BP算法的海底管道腐蚀速率预测

管道腐蚀速率的影响因素众多且各因素协同作用,构成异常复杂的腐蚀体系,很难对其进行准确预测。针对单一BP模型由于初始权值和阈值的选取不当容易陷入局部最优等问题,引入WOA算法优化BP神经网络对海底管道腐蚀速率进行预测,并与GA和PSO算法优化BP预测模型进行对比,验证WOA-BP模型的预测效果和可行性。结果表明：WOA-BP模型的平均绝对百分误差和均方根误差分别为3.689%和0.1537,远低于单一BP、PSO-BP、GA-BP模型,具有较高的预测精度和稳定性,可以为海底管道内腐蚀防护和油气管道流动保障提供决策支持。     

几何代数GIS计算引擎的设计与实现

分析型GIS的发展对GIS开发模式的统一性和算法的通用性提出了新的要求。基于几何代数(GA)的GIS多维统一计算模型(GA-MUC)为进行复杂GIS空间分析提供了解决思路。该文提出了基于GA的多维统一GIS计算引擎(GA-MUCE),探讨了基于GA的属性空间、网络空间与场空间中基本对象与算子的表达,并将其用于GIS算法的构建。该引擎具有自适应性特征,可根据具体的应用需求定义计算空间,进而设计相应的对象表达与算子、算法库,最终构建插件形式的GIS分析求解模板。该研究是对复杂GIS空间分析问题求解模式的探索,有利于促进新一代分析型GIS的发展。