共查询到19条相似文献，搜索用时 406 毫秒
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含水层非均质结构的马尔可夫链地质统计方法及应用 总被引：5，自引：0，他引：5
针对含水层非均质性对地下水流动和溶质运移模拟的准确性有着重要的影响,但非均质性的刻画十分困难的问题,介绍了马尔可夫转移概率理论方法及其在含水层非均质结构研究中的应用,利用Walther定律建立了三维马尔可夫链,并将该方法用于研究位于山西省介休市龙凤河冲积扇岩相的空间分布规律.结果表明该地区细颗粒具有在垂直方向上向上沉积、水平方向上向外沉积的趋势,细颗粒岩相与粗颗粒岩相之间的关联性强而粗颗粒岩相之间的关联性差.该方法的缺点在于Walther定律的应用可能会增加不确定性. 相似文献
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结合支持向量机和马尔可夫链,提出了一种新的位移时序预测模型--支持向量机－马尔可夫链预测模型（SVM-MC）。通过对实测位移值的学习,利用经粒子群算法优化的支持向量机对位移时间序列的宏观发展趋势进行滚动预测;在此基础上应用马尔可夫链确定位移时序的状态转移概率矩阵,通过对状态的划分、实测值与支持向量机拟合值的绝对误差及相对误差等指标的分析,实现了对预测结果的改进。将该模型应用到某工程永久船闸高边坡的位移时序预测中,结果表明,该模型具有科学可靠、预测精度高的优点,在岩土体位移时序预测中具有有一定工程应用价值。 相似文献
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以吉林省九台市气象站1977-2009年的逐月降水量资料为基础,利用滑动平均法、线性倾向估计法和M-K秩次相关法,分析了九台市近33年降水量变化趋势和突变特点。通过均值-标准差建立降水序列的分级标准,采用规范化的各自相关系数确定权重,利用滑动平均马尔可夫链模型,通过转移概率矩阵预测降水量。研究表明,九台市降水年内分配不均,主要集中夏季;降水量有减小的趋势;年降水量存在突变;滑动平均马尔可夫链模型预测精度较高,为降水量预报提供了一种方法。 相似文献
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本文以Ｗ１２－ｘ油田ＷＩＩＩ,ＩＶ段储层为例,依据Ｗ１２－ｘ油田的岩心、测井和地震资料,详细研究了Ｗ１２－ｗ油田ＷＩＩＩ,ＩＶ段储层的岩－电和井－震响应模型,分析了主力储层对应的电性特征和地震波,阻特征,建立了岩相－阻抗抗概率响应模型,在此基础上,提出了运用储地震属性约束随机模拟的一般研究流程,并据此进行了实例分析。 相似文献
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油藏描述与生产预测的随机方法 总被引：1，自引：0，他引：1
油藏地质属性的随机建模用于储层研究的下列方向：（１）从储层网格块图中生成有效的储层参数;（２）评价储层预测中的不确定性。本文用转换函数推导出公式方法,并介绍了一种以相对指示变量为基础的连续指示模拟运算的方式,另外,在一般指标均值的章节中,提到了节点渗透区域的随机映象对有效块渗透率的确定。上述理论在储层岩相和网格块渗透率方面的模拟中可以得到验证。另外还用图例对随机映象和油层参数在储层预测评价中的效果 相似文献
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鄂尔多斯盆地红河油田属于低孔、超低渗岩性油气藏.红河油田长8储层主要发育辫状河三角洲前缘水下分流河道和分流间湾两种沉积微相,并且在水下分流河道沉积微相中储层参数变化范围大,非均质性强,用常规的单一沉积相控建模方法无法对储层参数空间分布的非均质性进行充分表征.为了精确描述其储层参数及展布特征,以红河油田92井区长812致密砂岩储层为例,在地质、岩心、测井等资料的基础上,应用基于精细岩相约束的储层参数建模方法建立了研究区三维地质模型,即先用沉积相发育模式约束建立沉积微相模型,再对水下分流河道沉积微相进行岩相细分,在沉积微相模型和不同岩相概率体的双重控制下建立岩相模型,然后以岩相模型为约束建立储层参数模型.结果表明,该方法建立的储层参数模型具有较高的可靠性,与地质认识符合较好,为该区油藏数值模拟提供了精确的地质模型,并为研究区下一步开发方案调整提供了地质依据. 相似文献
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岩相剖面的定量历史沉积学分析是以动力沉积学分析为基础、以数学地质为方法、以计算机为工具的定性-定量分析法.它主要包括马尔科夫链模型中的转移概率矩阵分析,置换分析,极限概率和马尔科夫链的首达时间分析,熵分析和时间序列分析,本文侧重从地质学的角度阐述了它们的原理和方法。 相似文献
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Q区块目的层为砂泥岩薄互层,常规方法预测的储层精度不能满足油田时下的需求。通过调研,认为薄互层具有各向异性,“两宽一高”地震数据的出现,为各向异性解释提供了数据基础。通过理论分析,采用分方位马尔科夫链地质统计反演预测储层展布规律。该方法将马尔科夫链转移概率与贝叶斯遗传概率相结合,利用地震波组特征进行约束,同时,方位信息的加入为储层预测精度的提高提供了选项。突破了以往只在碳酸盐岩及基岩各向异性反演的认知限制,使碎屑岩薄储层中的各向异性得以体现。通过理论与实践结合,证实了优势方位的储层预测精度优于其他方位。 相似文献
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Weidong Li 《Mathematical Geology》2007,39(3):321-335
Multi-dimensional Markov chain conditional simulation (or interpolation) models have potential for predicting and simulating
categorical variables more accurately from sample data because they can incorporate interclass relationships. This paper introduces
a Markov chain random field (MCRF) theory for building one to multi-dimensional Markov chain models for conditional simulation
(or interpolation). A MCRF is defined as a single spatial Markov chain that moves (or jumps) in a space, with its conditional
probability distribution at each location entirely depending on its nearest known neighbors in different directions. A general
solution for conditional probability distribution of a random variable in a MCRF is derived explicitly based on the Bayes’
theorem and conditional independence assumption. One to multi-dimensional Markov chain models for prediction and conditional
simulation of categorical variables can be drawn from the general solution and MCRF-based multi-dimensional Markov chain models
are nonlinear. 相似文献
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Weidong Li 《Mathematical Geosciences》2007,39(3):321-335
Multi-dimensional Markov chain conditional simulation (or interpolation) models have potential for predicting and simulating categorical variables more accurately from sample data because they can incorporate interclass relationships. This paper introduces a Markov chain random field (MCRF) theory for building one to multi-dimensional Markov chain models for conditional simulation (or interpolation). A MCRF is defined as a single spatial Markov chain that moves (or jumps) in a space, with its conditional probability distribution at each location entirely depending on its nearest known neighbors in different directions. A general solution for conditional probability distribution of a random variable in a MCRF is derived explicitly based on the Bayes’ theorem and conditional independence assumption. One to multi-dimensional Markov chain models for prediction and conditional simulation of categorical variables can be drawn from the general solution and MCRF-based multi-dimensional Markov chain models are nonlinear. 相似文献
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Updating Markov chain models using the ensemble Kalman filter 总被引：1，自引：0，他引：1
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Modeling Spatial Variability with One and Multidimensional Continuous-Lag Markov Chains 总被引：10，自引：0，他引：10
The continuous-lag Markov chain provides a conceptually simple, mathematically compact, and theoretically powerful model of spatial variability for categorical variables. Markov chains have a long-standing record of applicability to one-dimensional (1-D) geologic data, but 2- and 3-D applications are rare. Theoretically, a multidimensional Markov chain may assume that 1-D Markov chains characterize spatial variability in all directions. Given that a 1-D continuous Markov chain can be described concisely by a transition rate matrix, this paper develops 3-D continuous-lag Markov chain models by interpolating transition rate matrices established for three principal directions, say strike, dip, and vertical. The transition rate matrix for each principal direction can be developed directly from data or indirectly by conceptual approaches. Application of Sylvester's theorem facilitates establishment of the transition rate matrix, as well as calculation of transition probabilities. The resulting 3-D continuous-lag Markov chain models then can be applied to geo-statistical estimation and simulation techniques, such as indicator cokriging, disjunctive kriging, sequential indicator simulation, and simulated annealing. 相似文献
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Markov chain model to study the occurrence of pre-monsoon thunderstorms over Bhubaneswar,India 总被引：1，自引：0，他引：1
Surajit Chattopadhyay Nachiketa Acharya Goutami Chattopadhyay Siripurapu Kiran Prasad Uma Charan Mohanty 《Comptes Rendus Geoscience》2012,344(10):473-482
The present work deals with pre-monsoon thunderstorms over Bhubaneswar belonging to the state of Orissa, India. A Markovian approach has been adopted to discern the probabilistic behavior of the time series of the occurrence and non-occurrence of this hazardous weather event by introducing a dichotomy within the time series. After a painstaking analysis through chi-square tests, we have identified serial independence in a few years and first-order two-state Markovian dependence in a few years (2000, 2001, 2004 and 2006). Finally, for the years of first-order two-state Markovian dependence, it has been observed that the probability of occurrence or non-occurrence of thunderstorm gets higher if the state of the previous day is similar to that of the current day. Furthermore, the probability of getting non-thunderstorm day followed by non-thunderstorm day is higher than the probability of getting thunderstorm day followed by thunderstorm day. It has been also observed that the unconditional climatological probability of the occurrence of severe pre-monsoon thunderstorm implied by the Markov chain is closely in agreement with the observed relative frequencies. However, it could be revealed that Markov chain cannot, in general, be suggested as a predictive tool for pre-monsoon thunderstorms under study without investigating the serial dependence inherent in the time series. 相似文献
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A Fixed-Path Markov Chain Algorithm for Conditional Simulation of Discrete Spatial Variables 总被引：4，自引：0，他引：4
Weidong Li 《Mathematical Geology》2007,39(2):159-176
The Markov chain random field (MCRF) theory provided the theoretical foundation for a nonlinear Markov chain geostatistics.
In a MCRF, the single Markov chain is also called a “spatial Markov chain” (SMC). This paper introduces an efficient fixed-path SMC algorithm for conditional simulation of discrete spatial variables
(i.e., multinomial classes) on point samples with incorporation of interclass dependencies. The algorithm considers four nearest
known neighbors in orthogonal directions. Transiograms are estimated from samples and are model-fitted to provide parameter
input to the simulation algorithm. Results from a simulation example show that this efficient method can effectively capture
the spatial patterns of the target variable and fairly generate all classes. Because of the incorporation of interclass dependencies
in the simulation algorithm, simulated realizations are relatively imitative of each other in patterns. Large-scale patterns
are well produced in realizations. Spatial uncertainty is visualized as occurrence probability maps, and transition zones
between classes are demonstrated by maximum occurrence probability maps. Transiogram analysis shows that the algorithm can
reproduce the spatial structure of multinomial classes described by transiograms with some ergodic fluctuations. A special
characteristic of the method is that when simulation is conditioned on a number of sample points, simulated transiograms have
the tendency to follow the experimental ones, which implies that conditioning sample data play a crucial role in determining
spatial patterns of multinomial classes. The efficient algorithm may provide a powerful tool for large-scale structure simulation
and spatial uncertainty analysis of discrete spatial variables. 相似文献
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We present a method for fitting trishear models to surface profile data, by restoring bedding dip data and inverting for model parameters using a Markov chain Monte Carlo method. Trishear is a widely-used kinematic model for fault-propagation folds. It lacks an analytic solution, but a variety of data inversion techniques can be used to fit trishear models to data. Where the geometry of an entire folded bed is known, models can be tested by restoring the bed to its pre-folding orientation. When data include bedding attitudes, however, previous approaches have relied on computationally-intensive forward modeling. This paper presents an equation for the rate of change of dip in the trishear zone, which can be used to restore dips directly to their pre-folding values. The resulting error can be used to calculate a probability for each model, which allows solution by Markov chain Monte Carlo methods and inversion of datasets that combine dips and contact locations. These methods are tested using synthetic and real datasets. Results are used to approximate multimodal probability density functions and to estimate uncertainty in model parameters. The relative value of dips and contacts in constraining parameters and the effects of uncertainty in the data are investigated. 相似文献