川滇交界地区构造应力场及强震趋势的统计预测

本文对川滇地区中、强震震源机制解和中小地震平均节面解分析，指出川滇地区现今构造应力场在北西－北西西向压应力作用下，以水平剪切错动为主。同时，采用极值理论、最大信息熵、线性预测和灰色理论等方法综合分析，预测川滇地区未来强震的趋势。     

川滇当前强震活动图象与地震趋势

本在研究中国大陆中国南北地震带及川滇地震区新的经震活动幕（１９８０－１９９６年前后）主要强震活动图象特征及其变化的基础上,提出了地中短期强震趋势预测的几项强震活动图象指标,据此川滇当前强震趋势作了初步的预测与讨论。     

康定,东川两地段强震活动对比及在川滇强震预测中的意义

本文提出了具有相似活动构造背景的强震多发段康定段和东川段。对两者历史强震迁移和与川滇大地震的紧密联系,作了较有说服力的多次震例证明,因而本文所论及的有关活动特征在川滇未来强震预测中有一定参考价值。     

川滇强震活动图像特征及其重复性研究

将川滇地震区活动断裂、历史地震与地震预测经验教训等方面资料,充实地震活动图像分析预测强震趋势的方法,总结出了10项地震活动异常作为预测依据,统称为"地震活动图像异常"。利用此研究成果,从川滇推广至中、外许多震例都可以确认有这些异常图像的存在,反映出大地震孕育过程晚期,即主震前中短期阶段区域及主震破裂区应变释放的演化特征,有一定的构造物理背景。对川滇地震活动图像特征的核心——重复性及其验证效果作了分类介绍,讨论了重复性特征的机理。对强震趋势预测有一定的应用价值。     

川滇菱形块体强震活动关联分析

通过对1700年以来川滇地区6.7级以上强震活动的分析,发现川滇菱形块体是川滇地区主要的强震活动区域,强震活动关联度较高,主要表现为:(1)川滇菱形块体为川滇地区地震活动关联的主体;(2)滇东与川西地区的强震活动存在一定的呼应关系;(3)川滇菱形块体将可能进入新一轮强震活跃期;(4)川滇菱形块体东边界地震活动的有序迁移可能是对块体运动的响应。     

川滇强震活动图像特征与趋势讨论

概述了川滇地震大形势的活动特点，根据川滇1948－2001年与1948－1965年强震活动图像的对比，对川滇强震趋势作为初步的讨论。     

川滇交界地段强震潜在危险区深部结构和孕震环境研究

正川滇地区位于青藏高原东南缘,其特殊的地震构造环境和频繁的强震活动特征表明,该地区是研究现今构造运动、大陆强震孕育背景和预测未来强震危险区的理想场所,对该区深部构造环境和介质物性特征的研究,将有助于探查川滇活动块体的深部构造环境和形变场特征,深化各向异性与构造变形作用的认识,以及研究块体内部和边界地震成因的深部构造背     

PI算法用于川滇-安达曼-苏门答腊地区7.0级以上强震危险性预测的回溯性检验

作为一个中长期地震预测方法,基于复杂系统统计物理的图象信息学PI算法近年来广受关注．针对7级以上强震成组和突发交替的川滇地区,考虑将与其构造和地震活动关系密切,且强震频发的安达曼-苏门答腊地区作为统一的强震预测研究区,使用PI算法进行MW7.0及以上预测ldquo;目标震级rdquo;的地震危险性分析．计算中使用了1973年以来的NEIC目录,采用10年尺度的地震活动ldquo;异常学习rdquo;时段和3年尺度ldquo;预测时间窗rdquo;,对预测效果进行了ROC检验．回溯性研究显示,PI预测效果较好,表明将川滇-安达曼-苏门答腊地区作为统一的7以上强震PI预测研究区在统计上具有合理性．从统计物理角度,研究区组合前后的各态遍历性曲线显示,组合后的研究区对PI的适用程度虽不优于单独考虑川滇地区,但优于安达曼-苏门答腊地区．PI图象显示,2008震前可能存在中长期尺度的ldquo;前兆性rdquo;地震活动异常．      

关于川滇地区深部结构与强震活动关系的研究

川滇地区是中国大内部地震活动最强的地区之一,且近期的强震活动仍十分频繁。本文论述了川滇地区在我国大陆强震的机理和预测研究中的重要地位,回顾了３０我为在该地区开展的地震学研究,并提出在“９７３”国家基础研究计划中拟开展的深部地球物理探测及其相关的研究工作。     

川滇M≥6.7强震的二维坐标系与时间预测研究

20世纪以来,川滇地区M≥6.7强震时间间隔具有良好的规律性,其地震发生的年份可组成一个二维时间坐标系,并据此建立川滇地区的强震时间预测模型。预测分析表明：未来16年,川滇地区可能存在发生4次6.7级以上地震发生的风险,2012-2021及2025-2029年均有M≥6.7强震信号,且未来强震可能发生在2014-2015、2019与2027年前后。     

基于Markov链模型的相控随机建模

Markov链模型在储层随机建模中的作用越来越受到关注,但其多用于类型属性(岩相、沉积相、沉积亚相等)的模拟,对于连续型属性(孔隙度、渗透率、含油气饱和度等)的模拟还比较困难.本文提出用Markov链模型相控建模方法模拟连续型属性的思路,即首先用Markov链模型模拟出类型属性,其次在类型属性约束下模拟出连续型属性,从而解决连续型属性不能产生突变边界的问题.最后应用此方法进行了模拟实验,模拟结果显示不同岩相中孔隙度差异较大,而同种岩相中孔隙度变化较小,证明了此方法的可靠性和适用性.     

Discrete cosine transform for parameter space reduction in Bayesian electrical resistivity tomography

Electrical resistivity tomography is a non-linear and ill-posed geophysical inverse problem that is usually solved through gradient-descent methods. This strategy is computationally fast and easy to implement but impedes accurate uncertainty appraisals. We present a probabilistic approach to two-dimensional electrical resistivity tomography in which a Markov chain Monte Carlo algorithm is used to numerically evaluate the posterior probability density function that fully quantifies the uncertainty affecting the recovered solution. The main drawback of Markov chain Monte Carlo approaches is related to the considerable number of sampled models needed to achieve accurate posterior assessments in high-dimensional parameter spaces. Therefore, to reduce the computational burden of the inversion process, we employ the differential evolution Markov chain, a hybrid method between non-linear optimization and Markov chain Monte Carlo sampling, which exploits multiple and interactive chains to speed up the probabilistic sampling. Moreover, the discrete cosine transform reparameterization is employed to reduce the dimensionality of the parameter space removing the high-frequency components of the resistivity model which are not sensitive to data. In this framework, the unknown parameters become the series of coefficients associated with the retained discrete cosine transform basis functions. First, synthetic data inversions are used to validate the proposed method and to demonstrate the benefits provided by the discrete cosine transform compression. To this end, we compare the outcomes of the implemented approach with those provided by a differential evolution Markov chain algorithm running in the full, un-reduced model space. Then, we apply the method to invert field data acquired along a river embankment. The results yielded by the implemented approach are also benchmarked against a standard local inversion algorithm. The proposed Bayesian inversion provides posterior mean models in agreement with the predictions achieved by the gradient-based inversion, but it also provides model uncertainties, which can be used for penetration depth and resolution limit identification.     

A study for improving precipitation occurrences modelling with a Markov chain

Daily precipitation occurrences and their monthly wet-days' sums of precipitation-measuring stations in Greece are modelled with a Markov chain. The order of the chain is taken to be seasonally varying in accordance with the precipitation station's meteorological conditions and geographical location. The modelling efficiency of the Markov chain is significantly improved when it is conjunctively used with a second-order autoregressive stochastic model fitted on the monthly wet-days' sums.     

Pulse generation models for daily intermittent streamflows / Modèles de génération d’impulsions pour des débits journaliers intermittents

Abstract

Abstract Generating pulses and then converting them into flow are two main steps of daily streamflow generation. Three pulse generation models have been proposed on the basis of Markov chains for the purpose of generating daily intermittent streamflow time series in this study. The first one is based on two two-state Markov chains, whereas the second uses a three-state Markov chain. The third model uses harmonic analysis and fits Fourier series to the three-state Markov chain. Results for a daily intermittent streamflow data series show a good performance of the proposed models.     

Modeling the stochastic dependence of air pollution index data

The air pollution index (API) is a common tool, which is often used for determining the quality of air in the environment. In this study, a discrete-time Markov chain model is applied for describing the stochastic behaviour of API data. The study reported in this paper is conducted based on the data collected from Klang city in Malaysia for a period of 3 years (2012–2014). Based on the API data, we considered a five-state Markov chain for depicting the five different states of the air pollution. We identified the Markov chain is an ergodic Markov chain and determined the limiting distribution for each state of the air pollution. In addition, we have identified the mean first passage time from one state to another. Based on the limiting distribution and the mean return time, we found that the risk of occurrences for unhealthy events is small. However, the risk remains notably troubling. Therefore, the standard of air quality in Klang falls within a margin that is considered healthy for human beings.     

A comparative study of nonlinear Markov chain models for conditional simulation of multinomial classes from regular samples

Simulating fields of categorical geospatial variables from samples is crucial for many purposes, such as spatial uncertainty assessment of natural resources distributions. However, effectively simulating complex categorical variables (i.e., multinomial classes) is difficult because of their nonlinearity and complex interclass relationships. The existing pure Markov chain approach for simulating multinomial classes has an apparent deficiency—underestimation of small classes, which largely impacts the usefulness of the approach. The Markov chain random field (MCRF) theory recently proposed supports theoretically sound multi-dimensional Markov chain models. This paper conducts a comparative study between a MCRF model and the previous Markov chain model for simulating multinomial classes to demonstrate that the MCRF model effectively solves the small-class underestimation problem. Simulated results show that the MCRF model fairly produces all classes, generates simulated patterns imitative of the original, and effectively reproduces input transiograms in realizations. Occurrence probability maps are estimated to visualize the spatial uncertainty associated with each class and the optimal prediction map. It is concluded that the MCRF model provides a practically efficient estimator for simulating multinomial classes from grid samples.     

Simulating hourly rainfall occurrence within an equatorial rainforest,Borneo Island / Simulation de l'occurrence de pluie horaire au sein de la forêt équatoriale,Ile de Bornéo

Abstract

The ability to simulate characteristics of the diurnal cycle of rainfall occurrence, and its evolution over the seasons is important to the forecasting of hydrological impacts resulting from land-use and climate changes within the humid tropics. This stochastic modelling study uses a generalized linear model (GLM) solution to second-order Markov chain models, as these discrete models are better at describing binary occurrence processes on an hourly time-scale than continuous-time approaches such as stochastic state-space models. We show that transition probabilities derived by the Markov chain method need to be time-varying rather than stationary to simulate the evolution of the diurnal cycle of rainfall occurrence over a Southeast Asian monsoon sequence. The conceptual and pragmatic links between discrete diurnal processes and continuous processes occurring over seasonal periods are thereby simulated within the same model.     

Critical drought analysis by second-order Markov chain

Exact probability distribution functions (PDF) of critical droughts in stationary second-order Markov chains are derived for finite sample lengths on the basis of the enumeration technique. These PDF are useful in predicting the possible critical drought durations that may result from any hydrologic phenomenon during any future period provided that the second-order Markov chain is representative of the underlying probability generation mechanism. Necessary charts are provided for the expectation and variance of critical droughts. The application of the developed methodology is given for three representative annual flow series from different parts of the world. It is observed that their critical droughts confirm well with the second-order Markov chain.     

Predicting cumulative sediment depostition in large reservoirs

Sediment deposition and its accumulation in a large resorvoir depends on the inflow and reservoir storage content, respectively. Because of this fact it is possible to model the cumulative deposition of sediment as an additive process defined on a bivariate Markov chain. Using the bivariate Markov chain model the mean and variance of the cumulative deposition of John Martin Reservoir, Colorado, U.S.A. are estimated and compared with observed sedimentation data.     

Predicting cumulative sediment depostition in large reservoirs

Sediment deposition and its accumulation in a large resorvoir depends on the inflow and reservoir storage content, respectively. Because of this fact it is possible to model the cumulative deposition of sediment as an additive process defined on a bivariate Markov chain. Using the bivariate Markov chain model the mean and variance of the cumulative deposition of John Martin Reservoir, Colorado, U.S.A. are estimated and compared with observed sedimentation data.