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.     

Oil and gas resource assessment: The linkage between geological analysis and discovery process model results

Two widely-used techniques to estimate the volume of remaining oil and gas resources are discovery process modeling and geologic assessment. Both were used in a recent national assessment of oil and gas resources of the United States. Parallel estimates were obtained for 27 provinces. Geological-based estimates can typically see into areas not available to discovery process models (that is areas with little or no exploration history) and thus, on average, yield higher estimates. However, a linear relation does exist between the mean estimates obtained from these two methods. In addition, other variables were found in a multiple regression model that explained much of the difference. Thus, it is possible to perform discovery process modeling and adjust the estimates to yield results that might be expected from geological-based assessments.     

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。     

THEORY AND PRACTICE FOR COMPILING THE ATLAS OF NATURAL DISASTERS IN CHINA

CompilinganatlasofnaturaldisastersinChinaisabasistoresearchintoregionaldisasterssystems,torevealthetemPOralandspatialpatternofnaturaldisasters,aswellastoestablishcountermeasuresopinstnamraldisasters.TaldngtheuAtlasOfNaturalDisastersinChina"**asanexample,thisarticleinquiresintotheoreticalandpracticalproblemsaboutcompilinganatlasofregionalnaturaldisasters.ThebasictheoryofcompilinganatlasOfregionaldisastershasbeenfOundedonthecombinati0nofthesciencesofdisasters,cart0graphyandregiotalmhy.Theref…     

抓“热点”、解“难点”，依法行政搞矿管

原达县地区是全省矿业活动最频繁的地区之一,也一度成了全省有名的“采煤混战”大区。近几年来,以《矿产资源法》为指针,抓住“重点”,调处“热点”,解决“难点”,以点带面,加强矿管行政执法工作,促进了全区矿业秩序的全面好转,矿业生产也相应得到健康发展。矿业产值占全区工业总产值的１５％左右,是全区的一大支柱产业,有力地促进了地方经济的发展。     

矿产预测中成矿信息的提取和强化

成矿信息的提取是矿产预测的重要手段,其方法的有效性直接影响到预测成果的可靠性。在成矿规律研究的基础上,有意识地干预模型的构成,突出与成矿有关的信息;抑制某些成矿意义不明显和属干扰的信息,有目的地使模型向反映成矿信息的方向逼近,提高模型与矿床实际赋存地质环境的吻合程度,均是成矿信息提取和强化的内涵。本文从研究实践中总结了先验约束模型和非先验约束模型的强化方法,在新疆阿勒泰地区的地质－找矿工作中已取得     

首都减灾圈的思考与建议

首都减灾圈,系首都减轻自然灾害预测防治圈。1991年12月20—21日在北京召开了《首都圈自然灾害及其减灾对策研讨会》。本文根据这次会议所提供的材料,在从整体上实现减灾的思想指导下,就首都减灾圈的“成灾背景”、“首都减灾圈的组成”、“首都减灾圈的灾害预测与防治状况”、“首都减灾圈的灾害关联性分析”、“首都减灾圈的减灾实效预估”和“首都减灾圈的减灾对策与实施”等六个带有共同性的问题,进行了讨论。供制定首都圈减灾方案时参考。     

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.