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。     

安庆铜矿综合治水工程及其评价

介绍安庆铜矿矿山的生产及水文地质概况，指出矿山生产中存在的水文地质、环境地质等问题，提出总体治理方案和实施措施，对治水工程进行了综合评价。     

准噶尔盆地西北缘侏罗系储层

准噶尔盆地西北缘侏罗系陆相碎屑岩储层的岩性差异较大，岩性对物性的控制作用明显。储层的孔隙类型多样，不同类型的孔隙在发育规模、丰度及有效性方面都存在显差异，次生溶蚀孔隙、原生粒间孔隙和残余粒间孔隙是最重要的有效孔隙类型。储层渗透率与孔隙度之间存在较好的半对数相关关系。通过设置一定的孔隙度和渗透率参数界线，对储层储集性能进行评价，将侏罗系储层的孔渗性能划分为5个级别，可与当前流行的砂岩储层孔渗性能分级相对应。通过对大量压汞参数样本的聚类分析，将储层的孔隙结构划分为4个类型。通过各类参数统计及曲线形态对比，对孔隙结构类型进行了定量结合定性的优劣评价。最后，结合孔渗性能级别、储集空间类型、孔隙结构类型、岩性等特征，对准噶尔盆地西北缘侏罗系储层进行了综合分类评价。     

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.     

Numerical evaluation of the damping‐solvent extraction method in the frequency domain

The damping‐solvent extraction method for the analysis of unbounded visco‐elastic media is evaluated numerically in the frequency domain in order to investigate the influence of the computational parameters—domain size, amount of artificial damping, and mesh density—on the accuracy of results. An analytical estimate of this influence is presented, and specific questions regarding the influence of the parameters on the results are answered using the analytical estimate and numerical results for two classical problems: the rigid strip and rigid disc footings on a visco‐elastic half‐space with constant hysteretic material damping. As the domain size is increased, the results become more accurate only at lower frequencies, but are essentially unaffected at higher frequencies. Choosing the domain size to ensure that the static stiffness is computed accurately leads to an unnecessarily large domain for analysis at higher frequencies. The results improve by increasing artificial damping but at a slower rate as the total (material plus artificial) damping ratio ζ_t gets closer to 0.866. However, the results do not deteriorate significantly for the larger amounts of artificial damping, suggesting that ζ_t≈0.6 is appropriate; a larger value is not likely to influence the accuracy of results. Presented results do not support the earlier suggestion that similar accuracy can be achieved by a large bounded domain with small damping or by a small domain with larger damping. Copyright © 2002 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.     

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。     

Evaluation of the Reduction in Uncertainty Obtained by Conditioning a 3D Stochastic Channel to Multiwell Pressure Data

A stochastic channel embedded in a background facies is conditioned to data observed at wells. The background facies is a fixed rectangular box. The model parameters consist of geometric parameters that describe the shape, size, and location of the channel, and permeability and porosity in the channel and nonchannel facies. We extend methodology previously developed to condition a stochastic channel to well-test pressure data, and well observations of the channel thickness and the depth of the top of the channel. The main objective of this work is to characterize the reduction in uncertainty in channel model parameters and predicted reservoir performance that can be achieved by conditioning to well-test pressure data at one or more wells. Multiple conditional realizations of the geometric parameters and rock properties are generated to evaluate the uncertainty in model parameters. The ensemble of predictions of reservoir performance generated from the suite of realizations provides a Monte Carlo estimate of the uncertainty in future performance predictions. In addition, we provide some insight on how prior variances, data measurement errors, and sensitivity coefficients interact to determine the reduction in model parameters obtained by conditioning to pressure data and examine the value of active and observation well data in resolving model parameters.     

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…