随机AMR模型的参数估计及其在几次强震中的应用

通过模拟强震前累积应变能释放具有加速特征地震目录的方法，系统地研究了随机AMR模型参数估计特征．在此基础上，应用AIC（Akaikes information criteria）准则，分别对中国和新西兰几次强震加速情况进行了考察，分析强震前地震活动类型， 有无加速，及何种因素引起的加速，同时探讨加速活动的区域分布特征和预测未来强震发生时间的可能途径．      

Parameter estimation of the stochastic AMR model and its application to the study of several strong earthquakes

Introduction According to many published papers, seismicity in time-space domain shows some characteristics, such as doughnut epicenter distribution (Mogi, 1969) or quiescence of seismic activity before large earthquakes (WANG, et al, 2002), and aftershock decay (Ogata, 1998). In recent years, more and more seismologists (Lynnr, Steven, 1990) have found that many strong earthquakes are preceded by enhancing regional seismicity and accelerating strain energy release (ZHANG, et al, 2001). T…     

Empirical Maximum Likelihood Kriging: The General Case

Although linear kriging is a distribution-free spatial interpolator, its efficiency is maximal only when the experimental data follow a Gaussian distribution. Transformation of the data to normality has thus always been appealing. The idea is to transform the experimental data to normal scores, krige values in the “Gaussian domain” and then back-transform the estimates and uncertainty measures to the “original domain.” An additional advantage of the Gaussian transform is that spatial variability is easier to model from the normal scores because the transformation reduces effects of extreme values. There are, however, difficulties with this methodology, particularly, choosing the transformation to be used and back-transforming the estimates in such a way as to ensure that the estimation is conditionally unbiased. The problem has been solved for cases in which the experimental data follow some particular type of distribution. In general, however, it is not possible to verify distributional assumptions on the basis of experimental histograms calculated from relatively few data and where the uncertainty is such that several distributional models could fit equally well. For the general case, we propose an empirical maximum likelihood method in which transformation to normality is via the empirical probability distribution function. Although the Gaussian domain simple kriging estimate is identical to the maximum likelihood estimate, we propose use of the latter, in the form of a likelihood profile, to solve the problem of conditional unbiasedness in the back-transformed estimates. Conditional unbiasedness is achieved by adopting a Bayesian procedure in which the likelihood profile is the posterior distribution of the unknown value to be estimated and the mean of the posterior distribution is the conditionally unbiased estimate. The likelihood profile also provides several ways of assessing the uncertainty of the estimation. Point estimates, interval estimates, and uncertainty measures can be calculated from the posterior distribution.     

The use of scale recursive estimation for short term quantitative precipitation forecast

Scale recursive estimation (SRE) is adopted for short term quantitative precipitation forecast (QPF). The precipitation field is modelled using a lognormal random cascade, well suited to properly represent the scaling properties of rainfall fields. To estimate the random cascade parameters, scale recursive maximum likelihood estimation (MLE) is carried out by the iterative expectation maximization (EM) algorithm. To illustrate the potentiality of the SRE, forecast of a synthetically generated rainfall time series is shown. Adaptive estimation of the process parameters is carried out and precipitation forecasts are issued. The forecasts from the SRE are compared with those from standard ARMA models, showing a good performance. The SRE is then adopted for forecasting of an observed half hourly precipitation series for a two day storm event in northern Italy. The SRE provides good performance and it can therefore be adopted as a tool for short term QPF.     

基于地物空间信息的浮动先验概率最大似然分类研究

利用遥感影像对地物进行分类识别时，既需要考虑地物波谱信息，也需要考虑其空间信息。现有遥感分类方法主要集中在利用像素的波谱信息，对各个像素进行独立分类，忽略了地物空间信息。考虑到传统最大似然分类（MLC）方法包括先验概率和条件概率密度函数两个核心环节，提出基于空间信息的浮动先验概率MLC方法，融合空间信息和波谱信息，以提高分类精度。在分析地物空间信息的基础上，总结了基于空间信息的浮动先验概率确定原则和依据，包括地物几何空间特征、情景特征、临近像素空间自相关定律、景观参数等，并设计了基于地物空间特征和临近像素空间自相关定律的浮动先验概率确定算法和分类流程。通过分类试验和误差矩阵分析，结果表明：基于空间特征和临近像素空间自相关定律的浮动先验概率MLC方法，能够融合地物的波谱信息和空间信息，克服最小距离、MLC等基于像素波谱信息的分类方法的缺点，显著提高地物分类精度。     

A linearly regressive Gamma Markov process

The linearly regressive Gamma Markov sequence is being increasingly used as a model for geophysical phenomena, one of the reasons being that it is possible to determine the distribution of (weighted or otherwise) cumulative sums of such a sequence. In this paper we show briefly how to simulate such a sequence and its seasonal extension; we also show how to estimate its parameters. It is shown that the estimates obtained by the method of moments do not have a high efficiency, whereas those obtained by a modified maximum likelihood method have an efficiency close to unity.     

基于Hopfield神经网络模型的遥感影像分类算法

针对遥感影像的分类特点，提出了一种基于Hopfield神经网络模型的遥感影像分娄算法。首先阐述了Hopfield神经网络的结构及其工怍原理，分析了Hopfield神经网络优化规则；然后在Hopfield神经网络通用模型基础上，实现了Hopfield神经网络的算法。实验结果表明，这种分类器具有较高的精度与效率，分类结果优于最大似然分类法。     

基于相拟性准则的SAR图像分割方法

提出一种以似然差函数作为相似性衡量标准的SAR图像分割方法。首先根据SAR图像的强度分布特性，通过仿真，发现两个具有相同分布的均匀区域合并成一个区域后，它们的似然差函数近似与区域的大小和均值无关，而仅与SAR图像的视数有关。在此基础上对两个相邻区域的似然差函数进行简化，获得它的概率密度函数的解析形式。选取一定的虚警率，计算出两个相邻区域之间存在边界的似然差函数的阈值。然后根据似然差函数和区域的形状的约束建立融合代价，使得所有可以融合的区域按照一定的顺序融合。当没有区域可以进一步融合时，就获得SAR图像的最终分割结果。     

共聚焦点道集偏移速度建模

偏移速度建模可在多种道集实现.基于等时原理和差异时移分析,提出了共聚焦点道集偏移速度建模方法.偏移速度的更新是利用约束参数迭代反演实现,模型的参数化主要依据实际情况而定.为适应横向变速,选用了垂直速度梯度参数.模型试算表明:偏移速度相对误差在0.25%范围内,反射层深度误差小于10m.     

基于决策树的遥感影像分类方法研究

基于决策树分类算法在遥感影像分类方面的深厚潜力。探讨了3种不同的决策树算法（UDT、MDT和HDT）。首先对决策树算法结构。算法理论进行了阐述：具体利用决策树算法进行遥感土地覆盖分类实验，并把获得的结果与传统统计分类法进行比较，研究表明，决策树分类法相对简单，明确，分类结构直观，有诸多优势。