方差体技术在地震勘探中的应用

三维地震数据体反映了地下一个规则网格的反射情况。当遇到地下有断层或某个局部区域地层不连续变化时，一些地震道的反射特征就会与附近地震道出现差异而导致地震首局部的不连续性。三维方差体技术就是求取三维数据体所有样点的方差值来反映这种不连续性信息。通过该技术在济宁2号井某采区的实际应用，可看出，方差体技术在三维地震信息的自动拾取以及提高地震资料解释成果精度等方面有明显优势，对断层、陷落柱有良好的自动识别能力。     

高速公路桥头跳车病害的分析与治理方法探讨

根据高速公路桥头跳车病害探地雷达检测结果,结合路基实际工程地质资料及沉降观测值,分析出造成此病害的原因是桥与路之间沉降差异所致,故搭板与垫层和垫层与路基之间产生了脱空,造成桥搭板、垫层碎裂而出现了桥头跳车病害.针对病害根源,采用了压密注浆进行搭板加固及重新摊铺沥青混合料进行调坡处理的治理方法.     

基于DEM的地形简化方法对比分析

地形简化作为一门重要的数据压缩技术已广泛应用于DEM。在大量简化算法中，地形简化指标作为地形简化的核心环节，其好坏直接关系到地形简化的好坏。本文对基于局部误差、曲率和法向量的五个地形简化指标进行了分析评价，用离散的高斯合成曲面来模拟真实DEM，以解析得到的高斯曲率作为地形简化指标“真值”，对比研究了在离散高斯曲面上得到的五个简化指标与解析所得“真值”，通过对各个指标“保特征性”可信度的分析，获得了对这五个指标的整体评价，最后，实例验证了本文结论的正确性。     

Landsat7 ETM＋影像的融合和自动分类研究

利用SFIM、MLT、HPF和修改的Brovey(MB)等遥感影像融合算法对Landsat 7 ETM 影像进行融合和自动分类研究，并就融合影像的光谱保真度、高频空间信息融人度和分类精度对这些方法进行评价。结果表明SFIM变换几乎完全保持了原始影像的光谱特点，并具有最高的平均分类精度；MB变换具有最高的高频空间信息融人度；MLT变换也具有较高的分类精度；只有HPF变换的各项指标都不突出。所有4种融合影像的分类精度都较原始影像的分类精度有明显的提高。这表明，源于同一传感器系统的不同分辨率影像的融合可以避免异源传感器融合影像所常见的各种参数、时相和配准误差，所以能够明显地提高影像的自动分类精度。     

内蒙古大青山地区寒武系与奥陶系之间的一个重要的层序界面

大青山地区寒武纪－奥陶纪地层分布零星，是由中生代逆冲推覆构造就位于此的一系列近东西向延展的构造岩片组成。下寒武统色麻沟组直接覆盖在太古界变质岩之上，缺失中元古界什那干群，寒武系与奥陶系之间呈假整合接触，标志着区内寒武纪与奥陶纪之间发生过一次抬升构造运动，其形成机制与古亚洲洋内的稳定陆快向华北板块拼贴事件有关，是早奥陶世末期华北板块整体抬升的序幕。这个层序界面的认识，对于恢复华北板块北部边缘构造演化历史具有重要意义。     

Parameter identification of inelastic structures under dynamic loads

The generalized model of differential hysteresis contains 13 control parameters with which it can curve‐fit practically any hysteretic trace. Three identification algorithms are developed to estimate the control parameters of hysteresis for different classes of inelastic structures. These algorithms are based upon the simplex, extended Kalman filter, and generalized reduced gradient methods. Novel techniques such as global search and internal constraints are incorporated to facilitate convergence and stability. Effectiveness of the devised algorithms is demonstrated through simulations of two inelastic systems with both pinching and degradation characteristics in their hysteretic traces. Owing to very modest computing requirements, these identification algorithms may become acceptable as a design tool for mapping the hysteretic traces of inelastic structures. Copyright © 2002 John Wiley & Sons, Ltd.     

A recursive decomposition algorithm for network seismic reliability evaluation

A new probabilistic analytical approach to evaluate seismic system reliability of large lifeline systems is presented in this paper. The algorithm takes the shortest path from the source to the terminal of a node weight or edge weight network as decomposition policy, using the Boolean laws of set operation and probabilistic operation principal, a recursive decomposition process then could be constructed. For a general weight network, the modified Torrieri method (NTR/T method) is introduced to combine with the suggested algorithm. Therefore, the recursive decomposition algorithm may be applied to evaluate the seismic reliability of general lifeline systems. A series of case studies, including a practical district electric power network system and a large urban water supply system, show that the suggested algorithm supplies a useful probabilistic analysis means for the seismic reliability evaluation of large lifeline systems. 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。     

遗传算法在边坡数值计算中的应用

改进了进化方向的遗传算法与有限元数值法，结合并研制了相应的软件。应用该软件对多类型岩土边坡进行弹性模量、内聚力、内摩擦角等参数反演分析，显示误差很小，收敛速度也很快，这说明改进进化方向遗传算法这种新型的优化算法在多类型岩土参数优化估计中具有独特的优势。     

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.