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1.
Basement-involved structures associated with reverse, vertical and normal faults commonly involve non-parallel shear within a triangular deformation (trishear) zone located on the front limbs of the structures. Deformation within the trishear zone is characterized by shear gradients and an associated decrease in the dips of the beds in stratigraphically higher units. Geometric models suggest that the layer-parallel strain within the trishear zone depends on the type of fault (normal, reverse, or vertical), the dip and throw of the fault, the dip of the anticlinal or synclinal axial surfaces, and the distance of any unit above the initial tip of the trishear zone, located at the basement-sediment contact. At any given location, reverse faults typically show increasing layer parallel shortening, followed by decreasing layer parallel shortening and a transition to extension, with increasing throw. The transition from contraction to extension occurs at lower values of throw for stratigraphically lower units and also for faults with smaller dips. Vertical and normal faults exhibit increasing layer-parallel extension of all units with increasing throw, with larger extension for stratigraphically lower units. Experimental models suggest that the trishear zone can expand with increasing fault throw. The strain within the trishear zones is accommodated largely by secondary faults, which are rotated with progressive deformation. The strain variations in the experiments closely mimic those predicted by the geometric models for reverse, vertical, and normal faults. 相似文献
2.
This paper presents a consistent Bayesian solution for data integration and history matching for oil reservoirs while accounting for both model and parameter uncertainties. The developed method uses Gaussian Process Regression to build a permeability map conforming to collected data at well bores. Following that, an augmented Markov Chain Monte Carlo sampler is used to condition the permeability map to dynamic production data. The selected proposal distribution for the Markov Chain Monte Carlo conforms to the Gaussian process regression output. The augmented Markov Chain Monte Carlo sampler allows transition steps between different models of the covariance function, and hence both the parameter and model space are effectively explored. In contrast to single model Markov Chain Monte Carlo samplers, the proposed augmented Markov Chain Monte Carlo sampler eliminates the selection bias of certain covariance structures of the inferred permeability field. The proposed algorithm can be used to account for general model and parameter uncertainties. 相似文献
3.
Geotechnical models are usually associated with considerable amounts of model uncertainty. In this study, the model uncertainty of a geotechnical model is characterised through a systematic comparison between model predictions and past performance data. During such a comparison, model input parameters (such as soil properties) may also be uncertain, and the observed performance may be subjected to measurement errors. To consider these uncertainties, the model uncertainty parameters, uncertain model input parameters and actual performance variables are modelled as random variables, and their distributions are updated simultaneously using Bayes’ theorem. When the number of variables to update is large, solving the Bayesian updating problem is computationally challenging. A hybrid Markov Chain Monte Carlo simulation is employed in this paper to decompose the high-dimensional Bayesian updating problem into a series of updating problems in lower dimensions. To increase the efficiency of the Markov chain, the model uncertainty is first characterised with a first order second moment method approximately, and the knowledge learned from the approximate solution is then used to design key parameters in the Markov chain. Two examples are used to illustrate the proposed methodology for model uncertainty characterisation, with insights, discussions, and comparison with previous methods. 相似文献
4.
Prediction and evaluation of pollution of the subsurface environment and planning remedial actions at existing sites may be useful for siting and designing new land-based waste treatment or disposal facilities. Most models used to make such predictions assume that the system behaves deterministically. A variety of factors, however, introduce uncertainty into the model predictions. The factors include model and pollution transport parameters and geometric uncertainty. The Monte Carlo technique is applied to evaluate the uncertainty, as illustrated by applying three analytical groundwater pollution transport models. The uncertainty analysis provides estimates of statistical reliability in model outputs of pollution concentration and arrival time. Examples are provided that demonstrate: (a) confidence limits around predicted values of concentration and arrival time can be obtained, (b) the selection of probability distributions for input parameters affects the output variables, and (c) the probability distribution of the output variables can be different from that of the input variables, even when all input parameters have the same probability distribution 相似文献
5.
为了开展寒旱山区典型流域融雪径流过程的研究,提高融雪径流模型（SRM）在山区融雪地区的水文过程模拟精度,本文选取新疆提孜那甫河流域作为典型研究区,在SRM径流计算基础上,加入合适的基流数据并进行不确定性分析。考虑4种常见的基流分割方法（数字滤波法、加里宁法、BFI法（滑动最小值法）和HYSEP（hydrograph separation program）法）,基于贝叶斯理论,采用马尔科夫链蒙特卡洛（MCMC）模拟进行参数不确定性分析,对使用不同基流数据SRM的融雪径流模拟表现进行综合评价。分析结果表明,基于加里宁基流分割方法的模型（SRMK）能够最佳地模拟研究区融雪径流过程（纳什系数NSE在识别期和验证期分别为0.866和0.721,大于其他对比模型）。MCMC模拟能够较好地识别SRM参数,获得可靠的参数后验概率分布。当实测降水资料缺乏或其代表性较差时,TRMM（tropical rainfall measuring mission）卫星数据能够描述研究区的降水过程特征。 相似文献
6.
模型不确定性研究是水文科学的重要课题。以尼泊尔Bagmati流域为案例,采用了马尔科夫链蒙托卡罗(Markov Chain Monte Carlo)、蒙托卡罗(Monte Carlo)和拉丁超立方体(Latin Hypercube)等三种方法,分析了水箱模型输出成果的不确定性,并将三种方法所获得参数不确定性进行了比较。另外,运用Meta-Gaussian模型计算了总体不确定性,在基于所采用的似然函数基础上,对由参数导致模型输出的不确定性和模型输出的总体不确定性进行了比较。结果显示,模型的不确定性比参数不确定性更为重要,同时也表明,尽管蒙托卡罗和拉丁超立方体两种模拟方法产生几乎相同的结果,但两者都与马尔科夫链蒙托卡罗方法有很大的不同。 相似文献
7.
A Bayesian linear inversion methodology based on Gaussian mixture models and its application to geophysical inverse problems are presented in this paper. The proposed inverse method is based on a Bayesian approach under the assumptions of a Gaussian mixture random field for the prior model and a Gaussian linear likelihood function. The model for the latent discrete variable is defined to be a stationary first-order Markov chain. In this approach, a recursive exact solution to an approximation of the posterior distribution of the inverse problem is proposed. A Markov chain Monte Carlo algorithm can be used to efficiently simulate realizations from the correct posterior model. Two inversion studies based on real well log data are presented, and the main results are the posterior distributions of the reservoir properties of interest, the corresponding predictions and prediction intervals, and a set of conditional realizations. The first application is a seismic inversion study for the prediction of lithological facies, P- and S-impedance, where an improvement of 30% in the root-mean-square error of the predictions compared to the traditional Gaussian inversion is obtained. The second application is a rock physics inversion study for the prediction of lithological facies, porosity, and clay volume, where predictions slightly improve compared to the Gaussian inversion approach. 相似文献
8.
Integrating production data under uncertainty by parallel interacting Markov chains on a reduced dimensional space 总被引：2，自引：0，他引：2
Thomas Romary 《Computational Geosciences》2009,13(1):103-122
In oil industry and subsurface hydrology, geostatistical models are often used to represent the porosity or the permeability
field. In history matching of a geostatistical reservoir model, we attempt to find multiple realizations that are conditional
to dynamic data and representative of the model uncertainty space. A relevant way to simulate the conditioned realizations
is by generating Monte Carlo Markov chains (MCMC). The huge dimensions (number of parameters) of the model and the computational
cost of each iteration are two important pitfalls for the use of MCMC. In practice, we have to stop the chain far before it
has browsed the whole support of the posterior probability density function. Furthermore, as the relationship between the
production data and the random field is highly nonlinear, the posterior can be strongly multimodal and the chain may stay
stuck in one of the modes. In this work, we propose a methodology to enhance the sampling properties of classical single MCMC
in history matching. We first show how to reduce the dimension of the problem by using a truncated Karhunen–Loève expansion
of the random field of interest and assess the number of components to be kept. Then, we show how we can improve the mixing
properties of MCMC, without increasing the global computational cost, by using parallel interacting Markov Chains. Finally,
we show the encouraging results obtained when applying the method to a synthetic history matching case. 相似文献
9.
Grain-size distribution data,as a substitute for measuring hydraulic conductivity(K),has often been used to get K value indirectly.With grain-size distribution data of 150 sets of samples being input data,this study combined the Artificial Neural Network technology(ANN)and Markov Chain Monte Carlo method(MCMC),which replaced the Monte Carlo method(MC)of Generalized Likelihood Uncertainty Estimation(GLUE),to establish the GLUE-ANN model for hydraulic conductivity prediction and uncertainty analysis.By means of applying the GLUE-ANN model to a typical piedmont region and central region of North China Plain,and being compared with actually measured values of hydraulic conductivity,the relative error ranges are between 1.55%and 23.53%and between 14.08%and 27.22%respectively,the accuracy of which can meet the requirements of groundwater resources assessment.The global best parameter gained through posterior distribution test indicates that the GLUEANN model,which has satisfying sampling efficiency and optimization capability,is able to reasonably reflect the uncertainty of hydrogeological parameters.Furthermore,the influence of stochastic observation error(SOE)in grain-size analysis upon prediction of hydraulic conductivity was discussed,and it is believed that the influence can not be neglected. 相似文献
10.
Slope stability assessment is a geotechnical problem characterized by many sources of uncertainty. Some of them, e.g., are connected to the variability of soil parameters involved in the analysis. Beginning from a correct geotechnical characterization of the examined site, only a complete approach to uncertainty matter can lead to a significant result. The purpose of this paper is to demonstrate how to model data uncertainty in order to perform slope stability analysis with a good degree of significance.
Once the input data have been determined, a probabilistic stability assessment (first-order second moment and Monte Carlo analysis) is performed to obtain the variation of failure probability vs. correlation coefficient between soil parameters. A first result is the demonstration of the stability of first-order second moment (FOSM) (both with normal and lognormal distribution assumption) and Monte Carlo (MC) solutions, coming from a correct uncertainty modelling. The paper presents a simple algorithm (Fuzzy First Order Second Moment, FFOSM), which uses a fuzzy-based analysis applied to data processing. 相似文献
11.
Bayesian lithology/fluid inversion—comparison of two algorithms 总被引：1，自引：0，他引：1
Algorithms for inversion of seismic prestack AVO data into lithology-fluid classes in a vertical profile are evaluated. The
inversion is defined in a Bayesian setting where the prior model for the lithology-fluid classes is a Markov chain, and the
likelihood model relates seismic data and elastic material properties to these classes. The likelihood model is approximated
such that the posterior model can be calculated recursively using the extremely efficient forward–backward algorithm. The
impact of the approximation in the likelihood model is evaluated empirically by comparing results from the approximate approach
with results generated from the exact posterior model. The exact posterior is assessed by sampling using a sophisticated Markov
chain Monte Carlo simulation algorithm. The simulation algorithm is iterative, and it requires considerable computer resources.
Seven realistic evaluation models are defined, from which synthetic seismic data are generated. Using identical seismic data,
the approximate marginal posterior is calculated and the exact marginal posterior is assessed. It is concluded that the approximate
likelihood model preserves 50% to 90% of the information content in the exact likelihood model. 相似文献
12.
13.
River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved. 相似文献
14.
基于Monte Carlo-BP神经网络TBM掘进速度预测 总被引：1，自引：0，他引：1
预测隧道工程中TBM掘进速度,主要有完全经验的、半理论半经验的模型和人工智能等方法,所用参数均为确定性的,未考虑参数存在的随机性,故导致预测结果的不准确性。基于此,提出了Monte Carlo-BP神经网络TBM掘进速度预测模型,着重考虑了一些重要输入参数的随机性, 其中输入参数重要性的大小通过粗糙集进行计算排序。采用Monte Carlo产生随机数时,由于参量的样本数据的有限,分布函数均采用阶梯形经验分布函数。如果采用的数据是来自不同类型的 TBM,则应当考虑机器性能参数,并重新对参数重要性进行排序。实例计算表明,Monte Carlo-BP神经网络模型预测结果和实测值总体趋势和均值比较一致。 相似文献
15.
Current studies have focused on selecting constitutive models using optimization methods or selecting simple formulas or models using Bayesian methods. In contrast, this paper deals with the challenge to propose an effective Bayesian-based selection method for advanced soil models accounting for the soil uncertainty. Four representative critical state-based advanced sand models are chosen as database of constitutive model. Triaxial tests on Hostun sand are selected as training and testing data. The Bayesian method is enhanced based on transitional Markov chain Monte Carlo method, whereby the generalization ability for each model is simultaneously evaluated, for the model selection. The most plausible/suitable model in terms of predictive ability, generalization ability, and model complexity is selected using training data. The performance of the method is then validated by testing data. Finally, a series of drained triaxial tests on Karlsruhe sand is used for further evaluating the performance. 相似文献
16.
This paper is an extension of the two-dimensional coupled Markov chain model developed by Elfeki and Dekking (2001) supplemented with extensive simulations. We focus on the development of various coupled Markov chains models: the so-called fully forward Markov chain, fully backward Markov chain and forward–backward Markov chain models. We addressed many issues such as: sensitivity analysis of optimal sampling intervals in horizontal and lateral directions, directional dependency, use of Walther’s law to describe lateral variability, effect of conditioning on number of boreholes on the model performance, stability of the Monte Carlo realizations, various implementation strategies, use of cross validation techniques to evaluate model performance and image division for statistically non-homogeneous deposits are addressed. The applications are made on three sites; two sites are located in the Netherlands, and the third is in the USA. The purpose of these applications is to show under which conditions the Markov models can be used, and to provide some guidelines for the practice. Entropy maps are good tools to indicate places where high uncertainty is present, so can be used for designing sampling networks to reduce uncertainty at these locations. Symmetric and diagonally dominant horizontal transition probabilities with proper sampling interval show plausible results (fits with geologists prediction) in terms of delineation of subsurface heterogeneous structures. Walther’s law can be utilised with a proper sampling interval to account for the lateral variability. 相似文献
17.
W. Christopher Carleton 《第四纪科学杂志》2021,36(1):110-123
Chronological uncertainty complicates attempts to use radiocarbon dates as proxies for processes such as human population growth/decline, forest fires and marine ingression. Established approaches involve turning databases of radiocarbon-date densities into single summary proxies that cannot fully account for chronological uncertainty. Here, I use simulated data to explore an alternative Bayesian approach that instead models the data as what they are, namely radiocarbon-dated event counts. The approach involves assessing possible event-count sequences by sampling radiocarbon date densities and then applying a Markov Chain Monte Carlo method to estimate the parameters of an appropriate count-based regression model. The regressions based on individual sampled sequences were placed in a multilevel framework, which allowed for the estimation of hyperparameters that account for chronological uncertainty in individual event times. Two processes were used to produce simulated data. One represented a simple monotonic change in event-counts and the other was based on a real palaeoclimate proxy record. In both cases, the method produced estimates that had the correct sign and were consistently biased towards zero. These results indicate that the approach is widely applicable and could form the basis of a new class of quantitative models for use in exploring long-term human and environmental processes. 相似文献
18.
Jing-Sen Cai Tian-Chyi Jim Yeh E-Chuan Yan Rui-Xuan Tang Jet-Chau Wen Shao-Yang Huang 《Landslides》2018,15(6):1193-1204
An adaptive sampling approach is proposed, which can sample spatially varying shear strength parameters efficiently to reduce uncertainty in the slope stability analysis. This approach employs a limit equilibrium model and stochastic conditional methodology to determine the likely sampling locations. Karhunen-Loève expansion is used to conduct the conditional Monte Carlo simulation. A first-order analysis is also proposed to ease the computational burden associated with Monte Carlo simulation. These approaches are then tested using borehole data from a field site. Results indicate that the proposed adaptive sampling approach is an effective and efficient sampling scheme for reducing uncertainty in slope stability analysis. 相似文献
19.
In an attempt to derive more information on the parameters driving compaction, this paper explores the feasibility of a method
utilizing data on compaction-induced subsidence. We commence by using a Bayesian inversion scheme to infer the reservoir compaction
from subsidence observations. The method’s strength is that it incorporates all the spatial and temporal correlations imposed
by the geology and reservoir data. Subsequently, the contributions of the driving parameters are unravelled. We apply the
approach to a synthetic model of an upscaled gas field in the northern Netherlands. We find that the inversion procedure leads
to coupling between the driving parameters, as it does not discriminate between the individual contributions to the compaction.
The provisional assessment of the parameter values shows that, in order to identify adequate estimate ranges for the driving
parameters, a proper parameter estimation procedure (Markov Chain Monte Carlo, data assimilation) is necessary. 相似文献
20.
Image-based 3D modeling has recently opened the way to the use of virtual outcrop models in geology. An intriguing application of this method involves the production of orthorectified images of outcrops using almost any user-defined point of view, so that photorealistic cross-sections suitable for numerous geological purposes and measurements can be easily generated. These purposes include the accurate quantitative analysis of fault-fold relationships starting from imperfectly oriented and partly inaccessible real outcrops. We applied the method of image-based 3D modeling and orthorectification to a case study from the northern Apennines, Italy, where an incipient extensional fault affecting well-layered limestones is exposed on a 10-m-high barely accessible cliff. Through a few simple steps, we constructed a high-quality image-based 3D model of the outcrop. In the model, we made a series of measurements including fault and bedding attitudes, which allowed us to derive the bedding-fault intersection direction. We then used this direction as viewpoint to obtain a distortion-free photorealistic cross-section, on which we measured bed dips and thicknesses as well as fault stratigraphic separations. These measurements allowed us to identify a slight difference (i.e. only 0.5°) between the hangingwall and footwall cutoff angles. We show that the hangingwall strain required to compensate the upward-decreasing displacement of the fault was accommodated by this 0.5° rotation (i.e. folding) and coeval 0.8% thickening of strata in the hangingwall relatively to footwall strata. This evidence is consistent with trishear fault-propagation folding. Our results emphasize the viewpoint importance in structural geology and therefore the potential of using orthorectified virtual outcrops. 相似文献