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.     

区域地下水信息管理系统研究

本文通过《河南省地下水动态信息管理系统》的建立,讨论了区域地下水信息管理系统的设计目标,数据结构和软件实现,着重介绍了系统的软件设计特点,最后指出了这种管理系统在水文地质部门中具有广泛的应用前景。     

A realistic model for point-sources imaged on array detectors: The model and initial results

We have constructed a computer model for simulation of point-sources imaged on two-dimensional detectors. An attempt has been made to ensure that the model produces data that mimic real data taken with 2-D detectors. To be realistic, such simulations must include randomly generated noise of the appropriate type from all sources (e.g. source, background, and detector). The model is generic and accepts input values for parameters such as pixel size, read noise, source magnitude, and sky brightness. Point-source profiles are then generated with noise and detector characteristics added via our model. The synthetic data are output as simple integrations (onedimensional), as radial slices (two-dimensional), and as intensity-contour plots (three-dimensional). Each noise source can be turned on or off so that they can be studied separately as well as in combination to yield a realistic view of an image. This paper presents the basic properties of the model and some examples of how it can be used to simulate the effects of changing image position, image scale, signal strength, noise characteristics, and data reduction procedures.Use of the model has allowed us to confirm and quantify three points: 1) The use of traditionalsize apertures for photometry of faint point-sources adds substantial noise to the measurement which can significantly degrade the quality of the observation; 2) The number of pixels used to estimate the background is important and must be considered when estimating errors; and 3) The CCD equation normally used by the astronomical community consistently overestimates the signal-to-noise obtainable by a measurement while a revised equation, discussed here, provides a better estimator.     

A combined study of macroseismic data and focal mechanisms applied to the West-Bohemian earthquake,Czechoslovakia

The main shock of the West-Bohemian earthquake swarm, Czechoslovakia, (magnitudem=4.5, depthh=10 km) exhibits an irregular areal distribution of macroseismic intensities 6° to 7° MSK-64. Four lobes of the 6° isoseismal are found and the maximum observed intensity is located at a distance of 8 km from the instrumentally determined epicentre. This distribution can be explained by the energy flux of the directS wave generated by a circular source, the hypocentral location and focal mechanism of which are taken from independent instrumental studies. The theoretical intensity, which is assumed to be logarithmically proportional to the integrated squared ground-motion velocity (i.e.,I=const+log v ² (t)dt), fits the observed intensity with an overall root-mean-square error less than 0.5°. It is important that the present intensity data can also be equally well explained by the isotropic source. The fit was attained by means of a horizontally layered model though large fault zones and an extended sedimentary basin suggest a significant lateral heterogeneity of the epicentral region. The results encourage a broader application of the simple modelling technique used.     

Bayesian Spatial Modelling of Gamma Ray Count Data

Gamma ray logging is a method routinely employed by geophysicists and environmental engineers in site geology evaluations. Modelling of gamma ray data from individual boreholes assists in the local identification of major lithological changes; modelling these data from a network of boreholes assists with lithological mapping and spatial stratigraphic correlation. In this paper we employ Bayesian spatial partition models to analyse gamma ray data spatially. In particular, a spatial partition is defined via a Voronoi tessellation and the mean intensity is assumed constant in each cell of the partition. The number of vertices generating the tessellation as well as the locations of vertices are assumed unknown, and uncertainty about these quantities is described via a hierarchical prior distribution. We describe the advantages of the spatial partition modelling approach in the context of smoothing gamma ray count data and describe an implementation that may be extended to the fitting of a more general model than a constant mean within each cell of the partition. As an illustration of the methodology we consider a data set collected from a network of eight boreholes, which is part of a geophysical study to assist in mapping the lithology of a site. Gamma ray logs are linked with geological information from cores and the spatial analysis of log data assists with predicting the lithology at unsampled locations.