基于局部采样MCMC方法的时移探地雷达反演

马尔科夫链蒙特卡洛方法(MCMC)是一种启发式的全局寻优算法,可以用来解决概率反演的问题.基于MCMC方法的反演不依赖于准确的初始模型,可以引入任意复杂的先验信息,通过对先验概率密度函数的采样来获得大量的后验概率分布样本,在寻找最优解的过程中可以跳出局部最优得到全局最优解.MCMC方法由于计算量巨大,应用难度较高,在地...     

一种基于高斯马尔可夫随机场的异常目标探测方法

利用高斯马尔可夫随机场模型描述像元的邻域相关性信息,并将这种邻域信息引入到局域异常探测器中,提出了一种顾及邻域信息的高光谱遥感影像局域异常目标探测算法。实验证明,该方法克服了传统异常探测方法仅仅利用光谱信息的不足,比经典的RX算法的探测效果更好,并且可以更有效地探测出大于一个像元的异常目标。     

基于Markov链的金川铜镍矿床超基性岩体侵位过程模拟及找矿启示

金川铜镍矿床是世界第三大铜镍硫化物岩浆矿床,现有研究表明其成矿模式为岩浆通道成矿,但是对于金川超基性岩体的侵位过程存在较大争议。为探索岩体的侵位过程,将岩浆侵位描述为马尔可夫(Markov)过程,提出一种基于Markov链的岩体侵位模拟算法,实现对金川超基性岩体侵位过程的模拟。以金川Ⅱ矿区为例,探讨了侵位过程与矿化的关联及岩浆通道骨架,为在矿床深部寻找第二成矿空间提供方向和线索。     

嵌入式马尔科夫链的地质属性建模与应用

传统的地质统计学所形成的算法理论和方法(如克里格算法)过分依赖样品的数据,变异函数参数众多,给地质模拟造成很大困难。基于马尔科夫链的地质属性建模采用转移概率描述样品区域的各种参数变量,通过转移概率矩阵直接推导地质属性分布比例、平均长度,其简化了地质空间各向异性处理过程,克服了传统地质统计学中参数众多且复杂难以计算和地质体分布过程中存在不对称性等缺陷,使得整个地质属性建模的过程更简洁、清晰,容易理解,且建模的结果可以很好地反映地质体空间分布的复杂性。该文利用马尔科夫链对南京市河西地区的新近地质层进行了地质属性建模,实例应用表明,使用该模型进行地质属性建模可为进一步的数值模拟提供支持。     

苏皖北部地区晚元古代地层的马尔柯夫链分析

本文运用数学地质理论中的马尔柯夫链分析方法,对苏皖北部地区及鲁西南地区晚元古代地层进行了初步研究。文中选择6条比较典型的剖面,分别对它们进行了转移概率、极限概率、差值阵、置换分析和熵分析等方面的计算,并对所得结果做了相应的地质解释。分析结果,从定量方面揭示出了晚元古代时期苏皖海盆基底的“波动”史及其上覆地层的主要沉积韵律类型,以及盆地内不同部位、不同时代岩性变化的主要趋势及其阶段性;并认为,在晚元古代早期苏皖海盆中很可能同时存在有南、北两个沉积中心,而并非像以往所认为的那样,只有一个沉积中心。     

改进支持向量机的高分遥感影像道路提取

针对支持向量机受分类数的限制在高分辨率遥感影像中无法直接获取高精度道路网信息的问题,该文提出一种新的混合的基于支持向量机的方法:首先,利用模糊C均值聚类方法将输入的遥感影像分为3类,以减少支持向量机的错分现象;其次,运用支持向量机将不同类别的像素分为道路类和非道路类;最后,应用马尔科夫随机场对分类结果进行噪声去除,并采用形态学进行后处理,进而得到精确道路网信息。实验结果表明:该算法不仅能够从高分辨率遥感影像中提取出道路网,而且精度优于直接使用支持向量机算法以及对比算法。     

改进灰色马尔科夫模型在基坑预测中的研究

基坑预测问题关系到工程施工的安全,在施工过程中对基坑进行周密的监测和变性预测分析显得尤为重要。针对传统预测模型存在固有偏差和可靠性低的缺点,采用新陈代谢的原理对无偏灰色加权马尔科夫模型进行改进。该模型先用无偏灰色模型拟合系统的总体变化趋势,然后,对拟合残差进行马尔可夫状态划分,并根据各阶权重对不同步长的转移矩阵进行加权处理,用加权后的无偏灰色马尔科夫模型进行预测。在每一步的预测中,利用新陈代谢的原理不断更新建模所使用的数据。将该模型用于基坑沉降预测,并通过实例进行验证。实验表明:基于新陈代谢的无偏灰色加权马尔科夫模型提高了基坑沉降预测的精度和可靠性,预测精度与未改进模型相比提高了8.54%。     

Combined analysis of precipitation and water deficit for drought hazard assessment

Abstract

The combined analysis of precipitation and water scarcity was done with the use of the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI), developed as a monthly, two-variable SPI-SRI indicator to identify different classes of hydrometeorological conditions. Stochastic analysis of a long-term time series (1966–2005) of monthly SPI-SRI indicator values was performed using a first-order Markov chain model. This provided characteristics of regional features of drought formation, evolution and persistence, as well as tools for statistical long-term drought hazard prediction. The study was carried out on two subbasins of the Odra River (Poland) of different orography and land use: the mountainous Nysa K?odzka basin and the lowland, agricultural Prosna basin. Classification obtained with the SPI-SRI indicator was compared with the output from the NIZOWKA model that provided identification of hydrological drought events including drought duration and deficit volume. Severe and long-duration droughts corresponded to SPI-SRI Class 3 (dry meteorological and dry hydrological), while severe but short-term droughts (lasting less than 30 days) corresponded to SPI-SRI Class 4 (wet meteorological and dry hydrological). The results confirm that, in Poland, meteorologically dry conditions often shift to hydrologically dry conditions within the same month, droughts rarely last longer than 2 months and two separate drought events can be observed within the same year.     

A Comparison of Four Precipitation Distribution Models Used in Daily Stochastic Models

Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simulations. For modeling daily precipitation in weather generators, first-order Markov chain-dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the perfor- mance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian infor- mation criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August.     

中国省域离婚率的时空格局及趋同演变研究

针对现有离婚率研究时间、空间割裂,整体性探讨不足及实证研究缺乏的问题,该文运用空间自相关、空间马尔可夫链等空间统计方法对中国大陆各省域1979—2012年离婚率数据的时空分布及趋同演变进行了研究,结果发现:除东北、西北地区离婚率一直居高不下之外,中国大陆高离婚率地区逐步向中部延伸并聚集,离婚率存在着较强的空间自相关且空间聚集现象明显;省域离婚率的"两极分化"现象和"俱乐部趋同"现象均经历了由弱到强再减弱的波动变化趋势。