共查询到20条相似文献，搜索用时 31 毫秒
1.
Eulogio Pardo-Igúzquiza 《Mathematical Geology》1999,31(1):47-65
This paper shows the application of the Bayesian inference approach in estimating spatial covariance parameters. This methodology is particularly valuable where the number of experimental data is small, as occurs frequently in modeling reservoirs in petroleum engineering or when dealing with hydrodynamic variables in groundwater hydrology. There are two main advantages of Bayesian estimation: firstly that the complete distribution of the parameters is estimated and, from this distribution, it is a straightforward procedure to obtain point estimates, confidence regions, and interval estimates; secondly, all the prior information about the parameters (information available before the data are collected) is included in the inference procedure through their prior distribution. The results obtained from simulation studies are discussed. 相似文献
2.
《地学前缘(英文版)》2021,12(5)
Rock mechanical parameters and their uncertainties are critical to rock stability analysis, engineering design, and safe construction in rock mechanics and engineering. The back analysis is widely adopted in rock engineering to determine the mechanical parameters of the surrounding rock mass, but this does not consider the uncertainty. This problem is addressed here by the proposed approach by developing a system of Bayesian inferences for updating mechanical parameters and their statistical properties using monitored field data, then integrating the monitored data, prior knowledge of geotechnical parameters,and a mechanical model of a rock tunnel using Markov chain Monte Carlo(MCMC) simulation. The proposed approach is illustrated by a circular tunnel with an analytical solution, which was then applied to an experimental tunnel in Goupitan Hydropower Station, China. The mechanical properties and strength parameters of the surrounding rock mass were modeled as random variables. The displacement was predicted with the aid of the parameters updated by Bayesian inferences and agreed closely with monitored displacements. It indicates that Bayesian inferences combined the monitored data into the tunnel model to update its parameters dynamically. Further study indicated that the performance of Bayesian inferences is improved greatly by regularly supplementing field monitoring data. Bayesian inference is a significant and new approach for determining the mechanical parameters of the surrounding rock mass in a tunnel model and contributes to safe construction in rock engineering. 相似文献
3.
针对泥石流灾害沟谷图像分类问题,文章对Resnet18网络进行改进,提出了一种改进的卷积神经网络模型。通过在网络结构中加入残差注意力模块,解决了原模型提取图像特征较差、边缘模糊的问题,改进后的网络能精确捕捉到泥石流灾害沟谷图像中的轮廓和内部山脊信息。此外,文章还对多种注意力机制结构进行了实验对比,分析其差异性,得出最适合泥石流灾害沟谷数据分类的注意力机制网络。实验表明改进后的网络模型在泥石流灾害沟谷图像的分类准确率达到75.42%,其分类性能在Resnet18网络模型的基础上提升了5.1%。 相似文献
4.
《国际地质力学数值与分析法杂志》2018,42(15):1765-1784
In this paper a fully probabilistic approach based on the Bayesian statistical method is presented to predict ground settlements in both transverse and longitudinal directions during gradual excavation of a tunnel. To that end, the convergence confinement method is adopted to give estimates of ground deformation numerically. Together with in situ measurements of the evolution of vertical deflections at selected points along the tunnel line, it allows for the construction of a likelihood function and consequently in the framework of Bayesian inference to provide posterior improved knowledge of model parameters entering the numerical analysis. In this regard, the Bayesian updating is first exploited in the material identification step and next used to yield predictions of ground settlement in sections along the tunnel line ahead of the tunnel face. This methodology thus makes it possible to improve original designs by utilizing an increasing number of data (measurements) collected in the course of tunnel construction. 相似文献
5.
6.
Parameter identification is one of the key elements in the construction of models in geosciences. However, inherent difficulties
such as the instability of ill-posed problems or the presence of multiple local optima may impede the execution of this task.
Regularization methods and Bayesian formulations, such as the maximum a posteriori estimation approach, have been used to
overcome those complications. Nevertheless, in some instances, a more in-depth analysis of the inverse problem is advisable
before obtaining estimates of the optimal parameters. The Markov Chain Monte Carlo (MCMC) methods used in Bayesian inference
have been applied in the last 10 years in several fields of geosciences such as hydrology, geophysics or reservoir engineering.
In the present paper, a compilation of basic tools for inference and a case study illustrating the practical application of
them are given. Firstly, an introduction to the Bayesian approach to the inverse problem is provided together with the most
common sampling algorithms with MCMC chains. Secondly, a series of estimators for quantities of interest, such as the marginal
densities or the normalization constant of the posterior distribution of the parameters, are reviewed. Those reduce the computational
cost significantly, using only the time needed to obtain a sample of the posterior probability density function. The use of
the information theory principles for the experimental design and for the ill-posedness diagnosis is also introduced. Finally,
a case study based on a highly instrumented well test found in the literature is presented. The results obtained are compared
with the ones computed by the maximum likelihood estimation approach. 相似文献
7.
George A. Griffiths 《Mathematical Geology》1982,14(1):65-75
Bayesian inference modeling may be applied to empirical stochastic prediction in geomorphology where outcomes of geomorphic processes can be expressed by probability density functions. Natural variations in process outputs are accommodated by the probability model. Uncertainty in the values of model parameters is reduced by considering statistically independent prior information on long-term, parameter behavior. Formal combination of model and parameter information yields a Bayesian probability distribution that accounts for parameter uncertainty, but not for model uncertainty or systematic error which is ignored herein. Prior information is determined by ordinary objective or subjective methods of geomorphic investigation. Examples involving simple stochastic models are given, as applied to the prediction of shifts in river courses, alpine rock avalanches, and fluctuating river bed levels. Bayesian inference models may be applied spatially and temporally as well as to functions of a random variable. They provide technically superior forecasts, for a given shortterm data set, to those of extrapolation or stochastic simulation models. In applications the contribution of the field geomorphologist is of fundamental quantitative importance. 相似文献
8.
We present a new Bayesian framework for the validation of models for subsurface flows. We use a compositional model to simulate CO2 storage in saline aquifers, comparing simulated saturations to observed saturations, together with a Bayesian analysis, to refine the permeability field. At the laboratory scale, we consider a core that is initially fully saturated with brine in a drainage experiment performed at aquifer conditions. Two types of data are incorporated in the framework: the porosity field in the entire core and CO2 saturation values at equally spaced core slices for several values of time. These parameters are directly measured with a computed tomography scanner. We then find permeability fields that (1) are consistent with the measured parameters and, at the same time, (2) allow one to predict future fluid flow. We combine high performance computing, Bayesian inference, and a Markov chain Monte Carlo (McMC) method for characterizing the posterior distribution of the permeability field conditioned on the available dynamic measurements (saturation values at slices). We assess the quality of our characterization procedure by Monte Carlo predictive simulations, using permeability fields sampled from the posterior distribution. In our characterization step, we solve a compositional two-phase flow model for each permeability proposal and compare the solution of the model with the measured data. To establish the feasibility of the proposed framework, we present computational experiments involving a synthetic permeability field known in detail. The experiments show that the framework captures almost all the information about the heterogeneity of the permeability field of the core. We then apply the framework to real cores, using data measured in the laboratory. 相似文献
9.
拟建猴子岩水电站移民安置点位于江口沟泥石流堆积扇上,通过现场调查泥石流形成条件和运动特征,获得了1958年发生的50 a一遇泥石流危险区范围,根据雨洪法计算确定了泥石流的相关运动学参数。使用基于有限体积法的CFX软件,选择Bingham流变模型对江口沟泥石流流动过程的液面分布情况和速度场进行了三维数值模拟,得出了泥石流危险区范围和速度场分布情况。模拟结果显示,江口沟50 a一遇泥石流流过堆积区的平均速度为5.76 m/s,其中最大速度为13.59 m/s。数值模拟计算得出的危险范围较1958年扩大,是因为早期泥石流堆积物将原有地面特别是沟道附近地面抬高,使得现状条件下泥石流更容易向两侧漫流泛滥而扩大。上述结果为泥石流防治工程设计及危险区范围划定提供了一种新的方法,对工程实践具有重要的指导意义。 相似文献
10.
受工程勘察成本及试验场地限制,可获得的试验数据通常有限,基于有限的试验数据难以准确估计岩土参数统计特征和边坡可靠度。贝叶斯方法可以融合有限的场地信息降低对岩土参数不确定性的估计进而提高边坡可靠度水平。但是,目前的贝叶斯更新研究大多假定参数先验概率分布为正态、对数正态和均匀分布,似然函数为多维正态分布,这种做法的合理性有待进一步验证。总结了岩土工程贝叶斯分析常用的参数先验概率分布及似然函数模型,以一个不排水黏土边坡为例,采用自适应贝叶斯更新方法系统探讨了参数先验概率分布和似然函数对空间变异边坡参数后验概率分布推断及可靠度更新的影响。计算结果表明：参数先验概率分布对空间变异边坡参数后验概率分布推断及可靠度更新均有一定的影响,选用对数正态和极值I型分布作为先验概率分布推断的参数后验概率分布离散性较小。选用Beta分布和极值I型分布获得的边坡可靠度计算结果分别偏于保守和危险,选用对数正态分布获得的边坡可靠度计算结果居中。相比之下,似然函数的影响更加显著。与其他类型似然函数相比,由多维联合正态分布构建的似然函数可在降低对岩土参数不确定性估计的同时,获得与场地信息更为吻合的计算结果。另外,构建似然函数时不同位置处测量误差之间的自相关性对边坡后验失效概率也具有一定的影响。 相似文献
11.
The Bayesian framework is the standard approach for data assimilation in reservoir modeling. This framework involves characterizing the posterior distribution of geological parameters in terms of a given prior distribution and data from the reservoir dynamics, together with a forward model connecting the space of geological parameters to the data space. Since the posterior distribution quantifies the uncertainty in the geologic parameters of the reservoir, the characterization of the posterior is fundamental for the optimal management of reservoirs. Unfortunately, due to the large-scale highly nonlinear properties of standard reservoir models, characterizing the posterior is computationally prohibitive. Instead, more affordable ad hoc techniques, based on Gaussian approximations, are often used for characterizing the posterior distribution. Evaluating the performance of those Gaussian approximations is typically conducted by assessing their ability at reproducing the truth within the confidence interval provided by the ad hoc technique under consideration. This has the disadvantage of mixing up the approximation properties of the history matching algorithm employed with the information content of the particular observations used, making it hard to evaluate the effect of the ad hoc approximations alone. In this paper, we avoid this disadvantage by comparing the ad hoc techniques with a fully resolved state-of-the-art probing of the Bayesian posterior distribution. The ad hoc techniques whose performance we assess are based on (1) linearization around the maximum a posteriori estimate, (2) randomized maximum likelihood, and (3) ensemble Kalman filter-type methods. In order to fully resolve the posterior distribution, we implement a state-of-the art Markov chain Monte Carlo (MCMC) method that scales well with respect to the dimension of the parameter space, enabling us to study realistic forward models, in two space dimensions, at a high level of grid refinement. Our implementation of the MCMC method provides the gold standard against which the aforementioned Gaussian approximations are assessed. We present numerical synthetic experiments where we quantify the capability of each of the ad hoc Gaussian approximation in reproducing the mean and the variance of the posterior distribution (characterized via MCMC) associated to a data assimilation problem. Both single-phase and two-phase (oil–water) reservoir models are considered so that fundamental differences in the resulting forward operators are highlighted. The main objective of our controlled experiments was to exhibit the substantial discrepancies of the approximation properties of standard ad hoc Gaussian approximations. Numerical investigations of the type we present here will lead to the greater understanding of the cost-efficient, but ad hoc, Bayesian techniques used for data assimilation in petroleum reservoirs and hence ultimately to improved techniques with more accurate uncertainty quantification. 相似文献
12.
An important issue of using the multiple-point (MP) statistical approach for reservoir modeling concerns the integration of
auxiliary constraints derived, for instance, from seismic information. There exist two methods in the literature for these
non-stationary MP simulations. One is based on an analytical approximation (the “τ-model”) of the conditional probabilities that involve auxiliary data. The degree of approximation with this method depends
on the parameter τ, whose inference is difficult in practice. The other method is based on the inference of these conditional probabilities
directly from training images. This method classifies the auxiliary data into a few classes. This classification is in general
arbitrary and therefore inconvenient in practice, especially in the case of continuous auxiliary constraints. In this paper,
we propose an alternative method for performing non-stationary MP simulations. This method accounts for the data support in
the modeling procedure and allows, in particular, continuous auxiliary data to be integrated into MP simulations. This method
avoids the major limitations of the previous methods, namely the use of an approximate analytical model and the reduction
of the auxiliary data into a limited number of classes. This method can be easily implemented in the existing MP simulation
codes. Numerical tests show good performance of this method both in reproducing the geometrical features of the training image
and in honouring the auxiliary data. 相似文献
13.
Model calibration and history matching are important techniques to adapt simulation tools to real-world systems. When prediction uncertainty needs to be quantified, one has to use the respective statistical counterparts, e.g., Bayesian updating of model parameters and data assimilation. For complex and large-scale systems, however, even single forward deterministic simulations may require parallel high-performance computing. This often makes accurate brute-force and nonlinear statistical approaches infeasible. We propose an advanced framework for parameter inference or history matching based on the arbitrary polynomial chaos expansion (aPC) and strict Bayesian principles. Our framework consists of two main steps. In step 1, the original model is projected onto a mathematically optimal response surface via the aPC technique. The resulting response surface can be viewed as a reduced (surrogate) model. It captures the model’s dependence on all parameters relevant for history matching at high-order accuracy. Step 2 consists of matching the reduced model from step 1 to observation data via bootstrap filtering. Bootstrap filtering is a fully nonlinear and Bayesian statistical approach to the inverse problem in history matching. It allows to quantify post-calibration parameter and prediction uncertainty and is more accurate than ensemble Kalman filtering or linearized methods. Through this combination, we obtain a statistical method for history matching that is accurate, yet has a computational speed that is more than sufficient to be developed towards real-time application. We motivate and demonstrate our method on the problem of CO2 storage in geological formations, using a low-parametric homogeneous 3D benchmark problem. In a synthetic case study, we update the parameters of a CO2/brine multiphase model on monitored pressure data during CO2 injection. 相似文献
14.
Comparison of numerical models of two debris flows in the Cortina d’ Ampezzo area,Dolomites, Italy 总被引：2，自引：0，他引：2
The accurate prediction of runout distances, velocities and the knowledge of flow rheology can reduce the casualties and property
damage produced by debris flows, providing a means to delineate hazard areas, to estimate hazard intensities for input into
risk studies and to provide parameters for the design of protective measures. The application of most of models that describe
the propagation and deposition of debris flow requires detailed topography, rheological and hydrological data that are not
always available for the debris-flow hazard delineation and estimation. In the Cortina d’Ampezzo area, Eastern Dolomites,
Italy, most of the slope instabilities are represented by debris flows; 325 debris-flow prone watersheds have been mapped
in the geomorphological hazard map of this area. We compared the results of simulations of two well-documented debris flows
in the Cortina d’Ampezzo area, carried on with two different single-phase, non-Newtonian models, the one-dimensional DAN-W
and the two-dimensional FLO-2D, to test the possibility to simulate the dynamic behaviour of a debris flow with a model using
a limited range of input parameters. FLO-2D model creates a more accurate representation of the hazard area in terms of flooded
area, but the results in terms of runout distances and deposits thickness are similar to DAN-W results. Using DAN-W, the most
appropriate rheology to describe the debris-flow behaviour is the Voellmy model. When detailed topographical, rheological
and hydrological data are not available, DAN-W, which requires less detailed data, is a valuable tool to predict debris-flow
hazard. Parameters obtained through back-analysis with both models can be applied to predict hazard in other areas characterized
by similar geology, morphology and climate. 相似文献
15.
F. Vagnon A. M. Ferrero G. Umili A. Segalini 《Geotechnical and Geological Engineering》2017,35(6):2663-2675
This paper discusses the applicability and the limitations of an approach to the limit states design of flexible barrier in which the soil/rock strength are factored as required in the European construction code. It shows as this approach has different implications if it is applied to the same kind of structure when loaded by different phenomena (rockfall and debris flow in particular). Flexible barriers are common countermeasures to protect from rockfall hazard and to restrain debris flow events. Even if an intense scientific production has demonstrated the difference between the two phenomena, the protection systems are still often designed in the same way. Additionally, the Eurocode 7 (EC7), which is the European Standard concerning geotechnical design, has not been conformed to these kinds of structures and consequently a relationship between the reliability of the system and the partial factors does not exist. Since most of the parameters that rule these systems are not even considered in the code, the Authors propose the study of two cases, in which rockfall and debris flow occur, respectively, to analyse the applicability and the limitations of EC7 principles to design the suitable kind of structure. 相似文献
16.
Optimization framework for calibration of constitutive models enhanced by neural networks 总被引：1，自引：0，他引：1
A two‐level procedure designed for the estimation of constitutive model parameters is presented in this paper. The neural network (NN) approach at the first level is applied to achieve the first approximation of parameters. This technique is used to avoid potential pitfalls related to the conventional gradient‐based optimization techniques, considered here as a corrector that improves predicted parameters. The feed‐forward NN (FFNN) and the modified Gauss–Newton algorithms are briefly presented. The proposed framework is verified for the elasto‐plastic modified Cam Clay model that can be calibrated based on standard triaxial laboratory tests, i.e. the isotropic consolidation test and the drained compression test. Two different formulations of the input data to the NN, enhanced by a dimensional reduction of experimental data using principal component analysis, are presented. The determination of model characteristics is demonstrated, first on numerical pseudo‐experiments and then on the experimental data. The efficiency of the proposed approach by means of accuracy and computational effort is also discussed. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
17.
泥石流是山区多发的一种地质灾害,它的发生和发展威胁着人们的生命和财产安全,影响着人们的正常生活,因而需要加强对其发生和发展过程的研究。结合泥石流的动力模型方程采用数值模拟方法再现泥石流发生和发展的过程,是研究和预测模拟泥石流灾害的有效手段。目前的动力模型方程大多只关注动力过程,却忽视了静动力过程的统一,这将导致在一些情况下产生错误的结果。本文研究了一维泥石流的静动力阻力特征,通过修正泥石流动力学方程的阻力项,得到了具有静动力统一特征的模型方程。并以Roe格式的近似Riemann解为基础,采用MUSCL线性重构方法建立了具有较高精度和分辨率的有限体积数值求解。具体算例的数值验证表明,方程阻力项的修正是合理的,所建立的数值求解也是稳定和有效的。 相似文献
18.
N. I. Akulov V. V. Akulova S. I. Shtel’makh M. N. Rubtsova P. A. Sholokhov 《Lithology and Mineral Resources》2018,53(1):36-52
Based on the catastrophic June 28, 2014, Arshan debris flows in the Eastern Sayan Mountains, the structure and lithological composition of the debris are studied and a debris flow defense system is proposed. Among five debris flows in this region, two flows 4.1 and 5.6 km long are scrutinized. The grain size and mineral composition of fans and mudflows, as well as their evolution scenario are studied. The paper also examines the engineering-geological features of debris flow sediments and their textural-structural and physical alterations in the course of settling. Specific attention is devoted to the lithological and climatic constraints of debris flows. It is noted that sediments of recent mudflows are characterized by a high underconsolidation and deliquescence, promoting the formation of high-plastic and fluidal zones that can migrate actively. The approach proposed for debris flow defense measures lies in the construction of flow diversion dams designed for orienting the debris flows toward the “debris dump site.” 相似文献
19.
Ibsen Chivatá Cárdenas 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2019,13(1):53-65
In this paper, we report on the use of Bayesian networks, BNs, learnt from data generated by physical and numerical models, to overcome to a certain degree a number of complications in traditional slope stability analyses that jointly consider the mechanical and hydraulic properties of soils. Discrete Bayesian networks resulted to be useful and efficient to acquire knowledge from simulated data and to identify significant factors by the combined use of backward inference and global sensitivity analysis. Further, BNs enable decision thresholds to be estimated quickly. Along with this, backward inference and global sensitivity analysis are performed in BNs at low computation costs. Moreover, under conditions in which knowledge is scarce, we show how a practitioner can be better informed using the proposed approach. All these previously under-reported modelling features in the specialised literature encourage the further application of the proposed approach to enhance slope stability analysis. 相似文献
20.
Probabilistic analysis of landslide potential of an inclined uniform soil layer of infinite length: application 总被引：1，自引：0，他引：1
An extended probabilistic model that is a modification of the Chen et al. (2007,) model for evaluating the failure probability
of an inclined soil layer with an infinite length was developed in the present paper, and then applied to evaluate the occurrence
probability of landslide-related debris flow in Tungmen gully located in the eastern Taiwan, which occurred a devastating
debris flow in 1990. The statistical properties of hydrogeological parameters were collected and summarized, and then used
to evaluate the landslide-related debris-flow probabilities at various relative water depths for Tungmen gully by using the
probabilistic model. Under the assumption that the soil is saturated, the soil’s cohesion is negligible and the specific gravity
of the solid particles of soils is a constant, a simplified probabilistic critical slope equation for the stability of an
infinite slope of soils was also developed, and used to estimate the occurrence probability of debris flow. The result shows
that probabilistic landslide analysis for an infinite slope could provide a suitable approximation for the risk analysis of
debris flow mobilization at a given gully. 相似文献