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51.
应用探地雷达探测活动断层   总被引:1,自引:0,他引:1  
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。  相似文献   
52.
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.  相似文献   
53.
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=CD, 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.  相似文献   
54.
参数的期望估计及其在形变分析中的应用   总被引:1,自引:0,他引:1  
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。  相似文献   
55.
FOS/DECAFE 91 (Fire of Savannas/Dynamique et Chimie Atmosphérique en Forêt Equatoriale) was the first multidisciplinary experiment organized in Africa to determine gas and aerosol emissions by prescribed savanna fires. The humid savanna of Lamto in Ivory Coast was chosen for its ecological characteristics representative of savannas with a high biomass density (900 g m–2 dry matter). Moreover the vegetation and the climate of Lamto have been studied for more than twenty years. The emission ratios (X/CO2) of the carbon compounds (CO2, CO, NMHC, CH4, PAH, organic acids and aerosols), nitrogen compounds (NOx, N2O, NH3 and soluble aerosols) and sulfur compounds (SO2, COS and aerosols) were experimentally determined by ground and aircraft measurements. To perform this experiment, 4 small plots (100×100 m) and 2 large areas (10×10 km) were prepared and burnt in January 1991 during the period of maximum occurrence of fires in this type of savanna. The detailed ecological study shows that the carbon content of the vegetation is constant within 1% (42 g C for 100 g of vegetal dry matter), the nitrogen content (0.29 g N for 100 g of dry matter) may vary by 10% and the sulfur content (0.05 g S/100 d.m.) by 20%. These variations of the biomass chemical content do not constitute an important factor in the variation of the gas and particle emission levels. With the emission ratios characteristic of humid savanna and flaming conditions (CO/CO2 of 6.1% at the ground and 8% for airborne measurements), we propose a set of new emission factors, taking into account the burning efficiency which is about 80%: 74.4% of the carbon content of the savanna biomass is released to the atmosphere in the form of CO2, 4.6% as CO, 0.2% as CH4, 0.5% as NMHC and 0.7% as aerosols. 17.2% of the nitrogen content of the biomass is released as NOx, 3.5% as N2O, 0.6% as NH3 and 0.5% as soluble aerosols.  相似文献   
56.
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 (Xn, n 0) or (Xt, t 0) for which the parameters are of interest and physically meaningful, and an observable process (Yn, n 0) or (Yt, 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.  相似文献   
57.
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)。  相似文献   
58.
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.  相似文献   
59.
In recent years, reclaimed wastewater as a water source for urban artificial landscape rivers is being rapidly developed in some coastal cities with limited water resource available in China. However, eutrophication occurs more often in the landscape waterbody with reclaimed wastewater than in natural rivers and lakes. The study of restoration and reconstruction of macrophytes, especially the submerged aquatic vegetation, is essential to inhibit eutrophication of reclaimed wastewater. This study investigated the seasonal variation of biomass of Ruppia maritima in the artificial landscape river using reclaimed wastewater as the only water source in Tianjin, China. It was found that there were two seasonal growth peaks of Ruppia maritima each year in the coastal reclaimedwastewater. The contents of nitrogen and phosphorus of Ruppia maritima varied with the season.  相似文献   
60.
There is a correspondence between flow in a reservoir and large scale permeability trends. This correspondence can be derived by constraining reservoir models using observed production data. One of the challenges in deriving the permeability distribution of a field using production data involves determination of the scale of resolution of the permeability. The Adaptive Multiscale Estimation (AME) seeks to overcome the problems related to choosing the resolution of the permeability field by a dynamic parameterisation selection. The standard AME uses a gradient algorithm in solving several optimisation problems with increasing permeability resolution. This paper presents a hybrid algorithm which combines a gradient search and a stochastic algorithm to improve the robustness of the dynamic parameterisation selection. At low dimension, we use the stochastic algorithm to generate several optimised models. We use information from all these produced models to find new optimal refinements, and start out new optimisations with several unequally suggested parameterisations. At higher dimensions we change to a gradient-type optimiser, where the initial solution is chosen from the ensemble of models suggested by the stochastic algorithm. The selection is based on a predefined criterion. We demonstrate the robustness of the hybrid algorithm on sample synthetic cases, which most of them were considered insolvable using the standard AME algorithm.  相似文献   
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