基于树形结构的地震数据库系统设计方法

地震观测数据的大量增长给数据管理提出了新的要求. 本文将树形结构引入到数据库设计中, 提出了基于树形结构设计关系数据库的方法. 该方法简单、 直观、 易于数据的组织, 可以简化数据库的设计过程. 在ldquo;长江三峡工程诱发地震监测系统地震监测总站数据库rdquo;设计中应用了该方法, 并取得了良好的效果.      

宽角反射地震波走时模拟的双重网格法

在研究地壳结构的人工源宽角反射地震资料解释中,常规宽角反射波走时和射线路径计算大都假定地壳模型为层状块状均匀介质.为了逼近实际地壳结构模型,要求模型尺度较大,为了提高地震资料解释的可靠性,须减小模型离散单元的尺寸,但同时计算量大大增加,使资料解释的效率较低.为此,本文尝试同时提高宽角反射地震资料解释效率和可靠性的方法,即使用双重网格计算宽角反射地震波走时和射线路径的最小走时树方法.双重网格法在均匀介质内部仅计算大网格节点,在速度变化点、震源点和检波点区域,同时计算小网格节点;在界面边界点使用比介质内部节点更大的子波传播区域.模型计算结果表明,对于大尺度的层状块状均匀介质模型,在保证精度的条件下,本文所提出的双重网格射线追踪方法的计算效率比单网格方法显著提高.     

自组织特征映射神经网络在测井岩性识别中的应用

为了解决测井岩性识别问题,引入具有较强的聚类和容错能力的自组织特征映射(SOFM)神经网络．在说明SOFM网络的模型和算法的基础上,结合某地的实际测井资料,建立SOFM网络岩性识别模型,进行岩性识别的应用研究．结果表明,识别的准确率较高,证明SOFM网络可以用于解决测井岩性识别问题,具有很好的应用前景．     

兖州矿区立井井筒非采动破裂的非线性预测与判别方法

厚冲积层立井井筒非采动破裂工程地质灾害是人类工程与自然环境相互作用的结果,该灾害的发生给煤矿造成巨大的经济损失,本文采用神经网络及模糊神经网络的方法对立井井筒破裂进行了预测与判别,预测与判别的结果表明,采用神经网络与模糊神经网络方法能够很好适用于立井井筒破裂的预测与判别,准确性高,能够满足实际应用的需要。     

Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley,Italy

This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instability developed through statistical models (conditional analysis and logistic regression), and neural network application, in order to better understand the relationship between the geological/geomorphological landforms and processes and landslide occurrence, and to increase the performance of landslide susceptibility models. The proposed experimental study concerns with a wide research project, promoted by the Tuscany Region Administration and APAT-Italian Geological Survey, aimed at defining the landslide hazard in the area of the Sheet 250 “Castelnuovo di Garfagnana” (1:50,000 scale). The study area is located in the middle part of the Serchio River basin and is characterized by high landslide susceptibility due to its geological, geomorphological, and climatic features, among the most severe in Italy. Terrain susceptibility to slope failure has been approached by means of indirect-quantitative statistical methods and neural network software application. Experimental results from different methods and the potentials and pitfalls of this methodological approach have been presented and discussed. Applying multivariate statistical analyses made it possible a better understanding of the phenomena and quantification of the relationship between the instability factors and landslide occurrence. In particular, the application of a multilayer neural network, equipped for supervised learning and error control, has improved the performance of the model. Finally, a first attempt to evaluate the classification efficiency of the multivariate models has been performed by means of the receiver operating characteristic (ROC) curves analysis approach.     

Forecasting of groundwater level in hard rock region using artificial neural network

In hardrock terrain where seasonal streams are not perennial source of freshwater, increase in ground water exploitation has already resulted here in declining ground water levels and deteriorating its’ quality. The aquifer system has shown signs of depletion and quality contamination. Thus, to secure water for the future, water resource estimation and management has urgently become the need of the hour. In order to manage groundwater resources, it is vital to have a tool to predict the aquifer response for a given stress (abstraction and recharge). Artificial neural network (ANN) has surfaced as a proven and potential methodology to forecast the groundwater levels. In this paper, Feed-Forward Network based ANN model is used as a method to predict the groundwater levels. The models are trained with the inputs collected from field and then used as prediction tool for various scenarios of stress on aquifer. Such predictions help in developing better strategies for sustainable development of groundwater resources.     

基于人工神经元网络的空间知识表达与推理方法研究

分析了智能决策支持系统的结构,通过比较指出了基于符号的知识表达与推理方法的不足,阐述了基于神经网络的知识表达与推理的方法,并认为神经网络方法可作为智能空间决策支持系统中符号方法的替代或补充,并通过实例验证了基于神经网络的不确定空间知识表达与推理的有效性.     

三维复杂介质中转换波走时快速计算

复杂介质中转换波走时计算是多波勘探地震学中重要内容之一.本项研究利用惠更斯原理和费玛原理,获得了三维复杂介质中转换波快速计算的改进型最小走时树方法.其中,在保证精度的条件下,为了提高三维转换波走时计算效率,首先对初至波最小走时树基本算法进行了改进.本方法通过将转换波分为上、下行波分别进行射线追踪以实现三维转换波走时的快速计算.模型计算表明,方法的计算速度快,而且稳定性强,对多波地震勘探具有较大的应用价值.     

一种基于神经网络的探地雷达信号解释研究

运用人工神经网络理论和方法,建立了用于隧道衬砌厚度探地雷达探测信号解释的BP神经网络模型,对某公路隧道衬砌检测厚度进行了分析应用,并与钻孔取芯结果进行比较,实践证明,该方法可提高探地雷达信号解释精度和工作效率．     

自适应GA-BP优化方法进行高分辨率反演

本文提出了一种高精度的神经网络学习方法．由于BP神经网络受初值的影响较大,而遗传算法可以很快达到全局最优解附近．本文结合遗传神经网络和BP神经网络各自的优点,将两种算法混合．使得神经网络的收敛速度得到加快,精度得到提高,并对传统的混合方法作了一点改进．通过对波阻抗进行反演的实践,表明这种方法可以达到提高分辨率的效果。