工业革命以来青藏高原与南极冰芯高分辨率甲烷记录对比研究

自工业革命以来全球大气CH4含量呈快速的增长趋势, 但达索普冰芯记录所显示的北半球中低纬度地区大气CH4增长的启动时间要晚于极地冰芯记录近100 a. 由于受北半球人类活动CH4排放、CH4在大气中的寿命及大气中CH4的传输等过程的影响, 最近150 a以来, 中低纬度大气CH4与南极大气CH4含量在不同的时段表现出不同的净积累量和增长速率, 且20世纪两次世界大战期间达索普冰芯记录明确显示出人类活动排放的减缓使大气CH4呈负增长. 对比研究认为, 到20世纪中叶人类活动的甲烷排放已达到极高值, 中低纬度大气中CH4增长率及年积累逐步趋于平稳且略有降低. 可以认定, 工业革命以来中低纬度地区大气CH4与南极大气CH4含量的变化在不同的时段在受控于人类活动影响的同时, CH4在大气中的行为和传输过程以及东亚中低纬度季风气候的影响决定了两地之间大气CH4含量变化存在显著的差异.     

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。     

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.     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS*

In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.     

辽宁省地震前兆台网技术改造概述

“九五”期间实施的《辽宁省地震前兆台网技术改造项目》使辽宁省地震前兆观测技术系统由模拟到数字实现了质的飞跃，本文对项目的技术思路，实施情况，取得的成果及建设特色进行了介绍。     

新疆数字地震前兆台网的数据传输功能分析

为了进一步提高地震前兆数据的传输效率和准确性 ,实现前兆数据采集和管理的自动化 ,对新疆地震局数字化地震前兆台网中心的功能、数据采集原理、数据传输特征进行了分析 ,认为数字化观测技术为地震前兆数据的采集、传输提供了一种高效、可靠的手段     

乌鲁木齐区域数字遥测台网各子台背景噪声分析

选取乌鲁木齐区域数字遥测台网中 1 1个子台的背景噪声数字记录 ,计算并分析其背景噪声地动速度均方根值 RMS、有效测量动态范围以及噪声功率谱 ,最后确定出各台址背景噪声相对集中的频段