DCT域遥感影像融合算法

提出一种基于离散余弦变换(Discrete Cosine Transform,DCT)与IHS变换的多光谱与全色遥感影像融合方法及其改进算法。本文方法依据DCT系数的特点在DCT域进行遥感影像融合,适合压缩格式的遥感影像快速融合。目视效果与客观评价表明,相比常用遥感影像融合方法,本文方法能在提高空间分辨率与保持光谱特性之间得到更好的折衷。     

应用探地雷达探测活动断层

在浅覆盖区采用探地雷达探测地震活动断层。通过实验确定出最佳的采集参数和数据处理流程,在雷达剖面上,能够清晰地显示出断层上部的形态特征、上断点埋深和岩土分层。结合钻孔资料,利用雷达剖面上对第四系覆盖的分层结果和上断点所在的层位可以分析和评价断层的活动性。     

小波边缘分析建模波阻抗反演方法在煤层解释中的应用

受限于地震波主波长的1／4,常规地震处理难以分辨较薄煤层。基于模型的波阻抗反演虽然克服了厚、薄煤层计算问题．但因难以得到准确的高频部分,很多情况下反演结果的精度和分辨率往往不能满足地质预测的要求。小波边缘分析建模波阻抗反演方法是从地震数据中提取地震特征参数,用于建模并参与迭代反演。该方法即可以弥补反演过程中高频成分的误差,减少对钻井数据的依赖程度,得到较为合理的初始模型,又可提高反演的精度和分辨率．使反演结果更好的反映实际地下地质情况。利用该方法,对大井-将军庙及新疆红沙泉2个勘探区的侏罗系中统西山窑组含煤地层进行波阻抗反演,反演结果与钻井资料进行比较,其中大井一将军庙0802及0809号孔单煤层厚度误差分别为3％、8％．新疆红沙泉多煤层厚度及煤层之间的夹层厚度预测结果除B6煤层误差较大外,其它误差均小于7％．可见该反演方法的计算结果比较准确。     

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.     

Estimation of covariance matrix from geochemical data with observations below detection limits

Multivariate statistical analyses have been extensively applied to geochemical measurements to analyze and aid interpretation of the data. Estimation of the covariance matrix of multivariate observations is the first task in multivariate analysis. However, geochemical data for the rare elements, especially Ag, Au, and platinum-group elements, usually contain observations the below detection limits. In particular, Instrumental Neutron Activation Analysis (INAA) for the rare elements produces multilevel and possibly extremely high detection limits depending on the sample weight. Traditionally, in applying multivariate analysis to such incomplete data, the observations below detection limits are first substituted, for example, each observation below the detection limit is replaced by a certain percentage of that limit, and then the standard statistical computer packages or techniques are used to obtain the analysis of the data. If a number of samples with observations below detection limits is small, or the detection limits are relatively near zero, the results may be reasonable and most geological interpretations or conclusions are probably valid. In this paper, a new method is proposed to estimate the covariance matrix from a dataset containing observations below multilevel detection limits by using the marginal maximum likelihood estimation (MMLE) method. For each pair of variables, sayY andZ whose observations containing below detection limits, the proposed method consists of three steps: (i) for each variable separately obtaining the marginal MLE for the means and the variances, , , , and forY andZ: (ii) defining new variables by and and lettingA=C+D andB=C–D, and obtaining MLE for variances, and forA andB; (iii) estimating the correlation coefficient _YZ by and the covariance _YZ by . The procedure is illustrated by using a precious metal geochemical data set from the Fox River Sill, Manitoba, Canada.     

A comparison between 2D and 3D wavelet analysis method for asteroid family dynamical study

As found in previous studies (Bendjoyaet al. 1991) the wavelet analysis is a reliable tool for the determination of asteroid families. A comparison between the 2D and a newly developed 3D wavelet analysis is performed on 14 fictitious families numerically generated the members of which area priori known. It clearly appears that the 3D method brings improvements to the 2D approach. Indeed the 3D analysis appears well suited for all cases even for those corresponding to the very dense Flora region and for exotic families such as filamentar ones. With the 2D method, satisfying results are obtained for the simulations which correspond to 70% of the real asteroid families previously found. The aim of this paper is to show quantitatively the improvement of the 3D method versus the 2D one.     

参数的期望估计及其在形变分析中的应用

利用“参数的期望估计”能准确定位、定量粗差及参数的期望估计不受粗差影响折特殊性质,寻找地壳变形区域和不变形区域,以确定拟稳点,进而进行拟稳变换,分析地菜变,将是一种有效方法。     

Estimation for partially observed Markov processes

Many stochastic process models for environmental data sets assume a process of relatively simple structure which is in some sense partially observed. That is, there is an underlying process (X_n, n 0) or (X_t, t 0) for which the parameters are of interest and physically meaningful, and an observable process (Y_n, n 0) or (Y_t, t 0) which depends on the X process but not otherwise on those parameters. Examples are wide ranging: the Y process may be the X process with missing observations; the Y process may be the X process observed with a noise component; the X process might constitute a random environment for the Y process, as with hidden Markov models; the Y process might be a lower dimensional function or reduction of the X process. In principle, maximum likelihood estimation for the X process parameters can be carried out by some form of the EM algorithm applied to the Y process data. In the paper we review some current methods for exact and approximate maximum likelihood estimation. We illustrate some of the issues by considering how to estimate the parameters of a stochastic Nash cascade model for runoff. In the case of k reservoirs, the outputs of these reservoirs form a k dimensional vector Markov process, of which only the kth coordinate process is observed, usually at a discrete sample of time points.     

成都城市发展对热岛强度影响的主成份回归分析

本文对成都市总人口、建成区面积等11个因子、作了主成份回归L-S估计和M-估计,讨论了成都城市发展对“热岛”强度的主要影响因子。结果表明,城区房屋建筑面积及总人口数是影响城市气候(气温)的主要因子,其次为城市人口总户数、建成面积等。 文中,对回归方程进行了拟合计算,回归效果比较满意(尤其是稳健回归)。     

Comparison of structural and least-squares lines for estimating geologic relations

Two different goals in fitting straight lines to data are to estimate a true linear relation (physical law) and to predict values of the dependent variable with the smallest possible error. Regarding the first goal, a Monte Carlo study indicated that the structural-analysis (SA) method of fitting straight lines to data is superior to the ordinary least-squares (OLS) method for estimating true straight-line relations. Number of data points, slope and intercept of the true relation, and variances of the errors associated with the independent (X) and dependent (Y) variables influence the degree of agreement. For example, differences between the two line-fitting methods decrease as error in X becomes small relative to error in Y. Regarding the second goal—predicting the dependent variable—OLS is better than SA. Again, the difference diminishes as X takes on less error relative to Y. With respect to estimation of slope and intercept and prediction of Y, agreement between Monte Carlo results and large-sample theory was very good for sample sizes of 100, and fair to good for sample sizes of 20. The procedures and error measures are illustrated with two geologic examples.