苏里格气田盒8气藏气水层测井识别研究

鄂尔多斯盆地苏里格气田盒8气藏为弹性驱动的河流相低效气藏,储层非均质性强,在开发中普遍见水。根据试采情况将气藏储层分为气层、水层、气水层三类,开展气、水层的测井识别研究。以区内26口井50个射孔层段各层的试油结果作为依据,选出了700个样本点作为判识建模的标本,以声波时差和中子孔隙度等8个原始测井值作为判识变量组合,采用模糊判别分析,建立了各层的判识模型。回判结果表明,模型对气层精度达到97％,水层为89％,气水层为94％。利用未参加建模的出水井射孔段对模型进行验证,判识结果基本符合实际情况,模型具有较高精度。     

基于GPS技术监测高层建筑物变形的研究

以武汉大学信息学部教学实验大楼为实验对象,分析了大楼在N、E、U方向上的每h变化、d变化及变形规律,阐明GPS技术在高层建筑变形监测中的应用。     

高速公路勘测定界的的方法研究

本文扼要阐述公路勘测定界测绘与其土地报批图件资料制作过程,借用现有商业软件南方"CASS成图系统"和办公系统软件微软"Microsoft Office Excel",成图、制表及图件资料涉及内容,提出应注意问题及建议。     

三维地震小面元采集技术在晋城矿区的应用

依托“西部煤炭资源高精度三维地震勘探技术”工程,对晋城矿区进行了旨在提高小断层,小陷落柱探测能力的高密度三维地震勘探。根据面元选择因素及该区地质任务,采用5m×5m网格进行野外数据采集;考虑炮检距、方位角、覆盖次数、排列片横纵比及煤层埋深（350～500m）等因素,采用中点放炮、60道接收,24次覆盖（横向4次,纵向6次）的8线16炮束状观测系统,基岩中激发。原始资料经同一处理流程后,获得5m×5m×1ms、5m×10m×1ms、10m×10m×1ms及2.5m×2.5m×1ms不同单元的三维数据体多个,通过对比可以发现小断层,小陷落柱在其小面元叠加时间剖面、顺层切片及相干切片都有清晰的反映。实例说明,小面元采集技术可以提高对小构造的纵、横向分辨能力,满足山区对三维地震精确勘探的要求。     

自动化监测技术在我国首条大型电力隧道中的应用

通过自动化监测在超大工程项目中的应用,利用现有技术手段,对工程进行有效管理,逐渐将对工程的安全、预判起到越来越重要的作用,它可以在为工程节约大量资金情况下,提高预判安全施工的能力,是提高工程管理的重要手段。     

Bayesian estimation of seismic hazard for two sites in Switzerland

Seismic hazard analysis is based on data and models, which both are imprecise and uncertain. Especially the interpretation of historical information into earthquake parameters, e.g. earthquake size and location, yields ambiguous and imprecise data. Models based on probability distributions have been developed in order to quantify and represent these uncertainties. Nevertheless, the majority of the procedures applied in seismic hazard assessment do not take into account these uncertainties, nor do they show the variance of the results. Therefore, a procedure based on Bayesian statistics was developed to estimate return periods for different ground motion intensities (MSK scale).Bayesian techniques provide a mathematical model to estimate the distribution of random variables in presence of uncertainties. The developed method estimates the probability distribution of the number of occurrences in a Poisson process described by the parameter . The input data are the historical occurrences of intensities for a particular site, represented by a discrete probability distribution for each earthquake. The calculation of these historical occurrences requires a careful preparation of all input parameters, i.e. a modelling of their uncertainties. The obtained results show that the variance of the recurrence rate is smaller in regions with higher seismic activity than in less active regions. It can also be demonstrated that long return periods cannot be estimated with confidence, because the time period of observation is too short. This indicates that the long return periods obtained by seismic source methods only reflects the delineated seismic sources and the chosen earthquake size distribution law.     

Generalized ‘dependencies’ and parameter variance

The autodependence, (a special case of the — now quite obsolete — dependences, which had been introduced for very specialized astrometric purposes) is proportional to the parameter variance which is the expectation of the variance of the systematic error of a function evaluated with estimated parameters.     

Estimation of statistical parameters for censored lognormal hydraulic conductivity measurements

Field tests of hydraulic conductivity (e.g., injection test, pumping test, etc.) in low permeability formations are subject to censoring due to the detection limit of the instruments used. An iterative method of estimating the mean and variance of hydraulic conductivity data with a presumed log-normal distribution function is presented. This method accounts for the data that are actually below the lower detection limit (called truncated data) and thus gives distribution parameters that are more representative for the underlying distribution. The proposed method is then tested on two simulated normally distributed random datasets having different variances. The results show that the means and variances estimated by the proposed method are very accurate. Finally, the method is used to estimate the mean and variance of hydraulic conductivity data from single hole water injection tests in a fractured geological formation.     

人工神经网络在白云岩类型识别中的应用

利用地球化学数据,运用人工神经网络方法对美国密苏里州东南Ｂｏｎｎｅｔｅｒｒｅ组（寒武纪）滨海相的白云岩进行了分类、识别,判别率达１００％,结果表明,该方法性能良好,可望成为岩石分类、判别的有效手段。     

Interpolation by regularized spline with tension: I. Theory and implementation

Bivariate and trivariate functions for interpolation from scattered data are derived. They are constructed by explicit minimization of a general smoothness functional, and they include a tension parameter that controls the character of the interpolation function (e.g., for bivariate case the surface can be tuned from a membrane to a thin steel plate), Tension can be applied also in a chosen direction, for modeling of phenomena with a simple type of anisotropy. The functions have regular derivatives of all orders everywhere. This makes them suitable for analysis of surface geometry and for direct application in models where derivatives are necessary. For processing of large datasets (thousands of data points), which are now common in geosciences, a segmentation algorithm with a flexible size of overlapping neighborhood is presented. Simple examples demonstrating flexibility and accuracy of the functions are presented.On leave from the Department of Physical Geography and Cartography, Comenius University, Mlynská dolina, Bratislava, Czechoslovakia.On leave from the Institute of Physics, Dúbravská cesta 9, Bratislava, Czechoslovakia.