矿产勘查与评估的统计地球化学方法

统计地球化学在矿产勘查与评估中的应用，主要包括采术规则，临界品位计算，资源量／储量公式，边界品位确定，网度判别，矿床经济分类以及矿床开发风险等内容。     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

Modelling evaporation using an artificial neural network algorithm

This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.     

利用ERS-2 SAR图像纹理分析方法揭示长白山天池火山近代喷发物空间分布特征

简单介绍了SAR图像的纹理特征以及正交小波变换纹理提取方法。论述了SAR图像的纹理特征参与分类的重要性。以长白山天池火山为例,通过对ERS2SAR图像进行纹理分析,提取了SAR图像两个层次的尺度变化、时频局部化和方向性纹理特征。并将SAR纹理特征与TM图像及DEM进行复合,利用多源信息各自的优势,进行了BP神经元网络分类,从较大范围对长白山天池火山735±15aB.P.大喷发的喷发物空间分布进行评价。获取了长白山天池火山近代喷发物的空间分布及规模。这对长白山天池火山未来喷发危险性初步评价、火山地质制图及火山灾害预测有重要意义。     

应用CP网络进行岩性识别

为通过测井解决岩性识别问题，引入了具有分类准确、算法简练等优点的CP（Counter-Propagation)网络。在详细介绍CP网络的网络模型和算法的基础上，结合某油田的实际测井资料，进行了CP网络识别研究。应用结果表明：CP网络训练周期短、识别准确率高、不存在收敛问题。通过试验研究得出结论：CP网络完全可以用于解决岩性识别等问题，具有广阔的应用前景。     

成矿系统与矿化网络研究

成矿系统研究体现了系统观和历史观的结合，是现代矿床学的一个发展趋势，本文简要总结了成矿系统及演化论的几个要点，即（1）按构造动力体制划分成矿系统大类；（2）我因耦合，临界转换的成矿作用机理；（3）矿床系列和异常系列构成的矿化网络；（4）矿床形成-变化-保存的演变过程，在此基础上，作者提出矿化网络是在一个成矿系统中形成的诸类矿床和相关异常的时空分布和结构，它是成矿系统的具体内容的表达。对成矿系统的深入研究应从矿化网络入手，着重在以下几个方面；（1）各类矿床的发育程度；（3）各类矿床的空间关系；（3）各类矿床的时间关系；（4）各类矿床的成因联系；（5）各类矿床被改造的情况。这些都是矿床学和找矿预测研究的基础内容。     

数字遥测地震台网地震速报中文平台

叙述了数字遥测地震台网地震速报中平台程序基本流程和使用特点，程序采用Vi编程方法实现以二维坐标点阵结构显示汉字，与台网的交互软件（EDSP-IAS）中的WaveView程序自动链接运行，该闰台界面操作方便直观，易于定位地震参数，自动速报上网和自动寻呼。     

Application of genetic BP network to discriminating earthquakes and explosions

Introduction Artificial Neural Network (ANN) is an important branch of artificial intelligence. It is proposed on the foundation of the study on modern neural science, is a man-made network that can implement some functions based on the mans comprehensive understanding for cerebral neural network (HAN, WANG, 1997). ANN is a mathematical model of simplified human brain neural network and is used to simulate the structures and functions of human brain neural network. ANN is a complex netw…     

Estimation of azimuth and slowness of tele-seismic signals recorded by a local seismic network

Introduction The azimuth and slowness are two major features of seismic signals. The accurate estimation of them is quite important for both phase identification and event location. Generally, there are two types of seismic stations, i.e. 3-component stations (3C) and arrays. To estimate the two direc-tional parameters, the polarization analysis (Jurkevics, 1988) is commonly used for 3C stations and the frequency-wavenumber spectrum analysis ( f-k) (Capon, 1969; Kvaerna, Doornbos, 1986) is …     

Fuzzy neural network models for liquefaction prediction

Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage, the training case histories are used to determine initial network parameters. In the final training stage, the training case histories are processed one by one to develop membership functions for the network parameters. During the testing phase, input variables are described in linguistic terms such as ‘high’ and ‘low’. The prediction is made in terms of a liquefaction index representing the degree of liquefaction described in fuzzy terms such as ‘highly likely’, ‘likely’, or ‘unlikely’. The results from the model are compared with actual field observations and misclassified cases are identified. The models are found to have good predictive ability and are expected to be very useful for a preliminary evaluation of liquefaction potential of a site for which the input parameters are not well defined.