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1.
In this work, we present an efficient matrix-free ensemble Kalman filter (EnKF) algorithm for the assimilation of large data
sets. The EnKF has increasingly become an essential tool for data assimilation of numerical models. It is an attractive assimilation
method because it can evolve the model covariance matrix for a non-linear model, through the use of an ensemble of model states,
and it is easy to implement for any numerical model. Nevertheless, the computational cost of the EnKF can increase significantly
for cases involving the assimilation of large data sets. As more data become available for assimilation, a potential bottleneck
in most EnKF algorithms involves the operation of the Kalman gain matrix. To reduce the complexity and cost of assimilating
large data sets, a matrix-free EnKF algorithm is proposed. The algorithm uses an efficient matrix-free linear solver, based
on the Sherman–Morrison formulas, to solve the implicit linear system within the Kalman gain matrix and compute the analysis.
Numerical experiments with a two-dimensional shallow water model on the sphere are presented, where results show the matrix-free
implementation outperforming an singular value decomposition-based implementation in computational time. 相似文献
2.
For the assessment of shallow landslides triggered by rainfall, the physically based model coupling the infinite slope stability
analysis with the hydrological modeling in nearly saturated soil has commonly been used due to its simplicity. However, in
that model the rainfall infiltration in unsaturated soil could not be reliably simulated because a linear diffusion-type Richards’
equation rather than the complete Richards’ equation was used. In addition, the effect of matric suction on the shear strength
of soil was not actually considered. Therefore, except the shallow landslide in saturated soil due to groundwater table rise,
the shallow landslide induced by the loss in unsaturated shear strength due to the dissipation of matric suction could not
be reliably assessed. In this study, a physically based model capable of assessing shallow landslides in variably saturated
soils is developed by adopting the complete Richards’ equation with the effect of slope angle in the rainfall infiltration
modeling and using the extended Mohr–Coulomb failure criterion to describe the unsaturated shear strength in the soil failure
modeling. The influence of rainfall intensity and duration on shallow landslide is investigated using the developed model.
The result shows that the rainfall intensity and duration seem to have similar influence on shallow landslides respectively
triggered by the increase of positive pore water pressure in saturated soil and induced by the dissipation of matric suction
in unsaturated soil. The rainfall duration threshold decreases with the increase in rainfall intensity, but remains constant
for large rainfall intensity. 相似文献
3.
Using a small ensemble size in the ensemble Kalman filter methodology is efficient for updating numerical reservoir models
but can result in poor updates following spurious correlations between observations and model variables. The most common approach
for reducing the effect of spurious correlations on model updates is multiplication of the estimated covariance by a tapering
function that eliminates all correlations beyond a prespecified distance. Distance-dependent tapering is not always appropriate,
however. In this paper, we describe efficient methods for discriminating between the real and the spurious correlations in
the Kalman gain matrix by using the bootstrap method to assess the confidence level of each element from the Kalman gain matrix.
The new method is tested on a small linear problem, and on a water flooding reservoir history matching problem. For the water
flooding example, a small ensemble size of 30 was used to compute the Kalman gain in both the screened EnKF and standard EnKF
methods. The new method resulted in significantly smaller root mean squared errors of the estimated model parameters and greater
variability in the final updated ensemble. 相似文献
4.
Apip Kaoru Takara Yosuke Yamashiki Kyoji Sassa Agung Bagiawan Ibrahim Hiroshi Fukuoka 《Landslides》2010,7(3):237-258
This paper describes the potential applicability of a hydrological–geotechnical modeling system using satellite-based rainfall
estimates for a shallow landslide prediction system. The physically based distributed model has been developed by integrating
a grid-based distributed kinematic wave rainfall-runoff model with an infinite slope stability approach. The model was forced
by the satellite-based near real-time half-hourly CMORPH global rainfall product prepared by NOAA-CPC. The method combines
the following two model outputs necessary for identifying where and when shallow landslides may potentially occur in the catchment:
(1) the time-invariant spatial distribution of areas susceptible to slope instability map, for which the river catchment is
divided into stability classes according to the critical relative soil saturation; this output is designed to portray the
effect of quasi-static land surface variables and soil strength properties on slope instability and (2) a produced map linked
with spatiotemporally varying hydrologic properties to provide a time-varying estimate of susceptibility to slope movement
in response to rainfall. The proposed hydrological model predicts the dynamic of soil saturation in each grid element. The
stored water in each grid element is then used for updating the relative soil saturation and analyzing the slope stability.
A grid of slope is defined to be unstable when the relative soil saturation becomes higher than the critical level and is
the basis for issuing a shallow landslide warning. The method was applied to past landslides in the upper Citarum River catchment
(2,310 km2), Indonesia; the resulting time-invariant landslide susceptibility map shows good agreement with the spatial patterns of
documented historical landslides (1985–2008). Application of the model to two recent shallow landslides shows that the model
can successfully predict the effect of rainfall movement and intensity on the spatiotemporal dynamic of hydrological variables
that trigger shallow landslides. Several hours before the landslides, the model predicted unstable conditions in some grids
over and near the grids at which the actual shallow landslides occurred. Overall, the results demonstrate the potential applicability
of the modeling system for shallow landslide disaster predictions and warnings. 相似文献
5.
Cheng Qiu Tetsuro Esaki Mowen Xie Yasuhiro Mitani Chunxiang Wang 《Environmental Geology》2007,52(8):1569-1579
A shallow landslide triggered by rainfall can be forecast in real-time by modeling the relationship between rainfall infiltration
and decrease of slope stability. This paper describes a promising approach that combines an improved three-dimensional slope
stability model with an approximate method based on the Green and Ampt model, to estimate the time–space distribution of shallow
landslide hazards. Once a forecast of rainfall intensity and slope stability-related data, e.g., terrain and geology data,
are acquired, this approach is shown to have the ability to estimate the variation of slope stability of a wide natural area
during rainfall and to identify the location of potential failure surfaces. The effectiveness of the estimation procedures
described has been tested by comparison with a one-dimensional method and by application to a landslide-prone area in Japan. 相似文献
6.
Shallow slope failure due to heavy rainfall during rainstorm and typhoon is common in mountain areas. Among the models used
for analyzing the slope stability, the rainwater infiltration model integrated with slope stability model can be an effective
way to evaluate the stability of slopes during rainstorm. This paper will propose an integrated Green–Ampt infiltration model
and infinite slope stability model for the analysis of shallow type slope failure. To verify the suitability of the proposed
model, seven landslide cases occurred in Italy and Hong Kong are adopted in this paper. The results indicate that the proposed
model can be used to distinguish failed and not-yet failed slopes. In addition, the proposed model can be used as the first
approximation for estimating the occurrence time of a rainfall-induced shallow landslide and its depth of sliding. 相似文献
7.
The impacts of hysteresis on variably saturated hydrologic response and slope failure 总被引：1，自引：1，他引：0
This investigation employs 3D, variably saturated subsurface flow simulation to examine hysteretic effects upon the hydrologic
response used to drive unsaturated slope stability assessments at the Coos Bay 1 (CB1) experimental catchment in the Oregon
Coast Range, USA. Slope stability is evaluated using the relatively simple infinite slope model for unsaturated soils driven
by simulated pore-water pressures for an intense storm that triggered a slope failure at CB1 on 18 November 1996. Simulations
employing both hysteretic and non-hysteretic soil–water retention curves indicate that using either the drying soil–water
retention curve or an intermediate soil–water retention curve that attempts to average the wetting and drying retention curves
underestimates the near-surface hydrologic response and subsequently the potential for slope failure. If hysteresis cannot
be considered in the hydrologic simulation, the wetting soil–water retention curve, which is seldom measured, should be used
for more physically based slope stability assessment. Without considering hysteresis or using the wetting soil–water retention
curve, the potential for landsliding in unsaturated materials may be underestimated and a slope failure could occur when simulations
predict stability. 相似文献
8.
Wiktoria Lawniczak Remus Hanea Arnold Heemink Dennis McLaughlin 《Computational Geosciences》2009,13(2):245-254
Reservoir management requires periodic updates of the simulation models using the production data available over time. Traditionally,
validation of reservoir models with production data is done using a history matching process. Uncertainties in the data, as
well as in the model, lead to a nonunique history matching inverse problem. It has been shown that the ensemble Kalman filter
(EnKF) is an adequate method for predicting the dynamics of the reservoir. The EnKF is a sequential Monte-Carlo approach that
uses an ensemble of reservoir models. For realistic, large-scale applications, the ensemble size needs to be kept small due
to computational inefficiency. Consequently, the error space is not well covered (poor cross-correlation matrix approximations)
and the updated parameter field becomes scattered and loses important geological features (for example, the contact between
high- and low-permeability values). The prior geological knowledge present in the initial time is not found anymore in the
final updated parameter. We propose a new approach to overcome some of the EnKF limitations. This paper shows the specifications
and results of the ensemble multiscale filter (EnMSF) for automatic history matching. EnMSF replaces, at each update time,
the prior sample covariance with a multiscale tree. The global dependence is preserved via the parent–child relation in the
tree (nodes at the adjacent scales). After constructing the tree, the Kalman update is performed. The properties of the EnMSF
are presented here with a 2D, two-phase (oil and water) small twin experiment, and the results are compared to the EnKF. The
advantages of using EnMSF are localization in space and scale, adaptability to prior information, and efficiency in case many
measurements are available. These advantages make the EnMSF a practical tool for many data assimilation problems. 相似文献
9.
The ensemble Kalman filter (EnKF) has been shown repeatedly to be an effective method for data assimilation in large-scale
problems, including those in petroleum engineering. Data assimilation for multiphase flow in porous media is particularly
difficult, however, because the relationships between model variables (e.g., permeability and porosity) and observations (e.g.,
water cut and gas–oil ratio) are highly nonlinear. Because of the linear approximation in the update step and the use of a
limited number of realizations in an ensemble, the EnKF has a tendency to systematically underestimate the variance of the
model variables. Various approaches have been suggested to reduce the magnitude of this problem, including the application
of ensemble filter methods that do not require perturbations to the observed data. On the other hand, iterative least-squares
data assimilation methods with perturbations of the observations have been shown to be fairly robust to nonlinearity in the
data relationship. In this paper, we present EnKF with perturbed observations as a square root filter in an enlarged state
space. By imposing second-order-exact sampling of the observation errors and independence constraints to eliminate the cross-covariance
with predicted observation perturbations, we show that it is possible in linear problems to obtain results from EnKF with
observation perturbations that are equivalent to ensemble square-root filter results. Results from a standard EnKF, EnKF with
second-order-exact sampling of measurement errors that satisfy independence constraints (EnKF (SIC)), and an ensemble square-root
filter (ETKF) are compared on various test problems with varying degrees of nonlinearity and dimensions. The first test problem
is a simple one-variable quadratic model in which the nonlinearity of the observation operator is varied over a wide range
by adjusting the magnitude of the coefficient of the quadratic term. The second problem has increased observation and model
dimensions to test the EnKF (SIC) algorithm. The third test problem is a two-dimensional, two-phase reservoir flow problem
in which permeability and porosity of every grid cell (5,000 model parameters) are unknown. The EnKF (SIC) and the mean-preserving
ETKF (SRF) give similar results when applied to linear problems, and both are better than the standard EnKF. Although the
ensemble methods are expected to handle the forecast step well in nonlinear problems, the estimates of the mean and the variance
from the analysis step for all variants of ensemble filters are also surprisingly good, with little difference between ensemble
methods when applied to nonlinear problems. 相似文献
10.
降雨持时较长且雨强较大时,雨水的入渗不仅会增大孔隙水压力,且易使张裂缝充水形成静水压力,对边坡稳定不利。结合降雨条件下饱和-非饱和渗流分析,考虑降雨过程中的瞬态孔隙水压力场与瞬态强度场,并同时考虑张裂缝充水时的静水压力,对边坡临界滑动场法进行改进,提出降雨条件下具有张裂缝边坡临界滑动场数值模拟方法,且对其进行了验证。将该方法用于一个典型均质黏土边坡算例,结果表明,（1）文中方法可考虑张裂缝具体位置、深度及其充水状态下对边坡稳定性的影响,并能搜索出任意形状危险滑面,计算结果合理可靠;（2）降雨条件下张裂缝中静水压力对边坡稳定性及滑面形状有较大影响;（3）降雨条件下张裂缝位置对边坡稳定有较大影响,距坡肩越近,张裂缝对边坡稳定性影响越大;（4）张裂缝处在最不利位置且充水时存在一个最不利深度。 相似文献
11.
顺序数据同化的Bayes滤波框架 总被引：6，自引：2，他引：4
数据同化是在动力学模型的运行过程中不断融合新的观测信息的方法论,Bayes理论是数据同化的基石.从原理、方法和符号系统为Bayes滤波在数据同化中的应用勾勒一个统一的框架.首先对连续数据同化和顺序数据同化的各种方法做了分类,然后给出了非线性系统顺序数据同化的Bayes递推滤波形式,并在此基础上介绍了典型的顺序数据同化方法--粒子滤波和集合Kalman滤波.粒子滤波实质上是一种基于递推Bayes估计和Monte Carlo模拟的滤波方法,而集合Kalman滤波相当于一种权值相等的粒子滤波.Bayes滤波理论为顺序数据同化提供了更广义的理论框架,从基础的数学理论上揭示了数据同化的基本原理. 相似文献
12.
降雨条件下浅层滑坡稳定性探讨 总被引：8，自引：0，他引：8
降雨条件下浅层滑坡是一种常见、多发的地质灾害现象,为了解边坡稳定性随降雨入渗过程的变化情况,以Green-Ampt入渗模型为基础,并考虑了动水压力的作用,建立了降雨入渗条件下浅层滑坡的概念模型,分别推导了降雨前有、无地下水位条件下的边坡安全系数与降雨时间的关系表达式。从分析结果中可以看出,对于这两种情况下边坡稳定性发生突变的主要原因归结于：前者为在湿润锋与地下水位面接触的短时间内,滑带处的孔隙水压力迅速增高;后者为滑带在浸水饱和情况下,岩土体的强度迅速降低。在此基础上,根据降雨过程中边坡是否达到饱和,提出边坡饱和临界时间的概念,考虑了初始降雨强度小于土壤入渗能力的情况。这个时间可以作为一个参数指标用于浅层滑坡的预警。 相似文献
13.
Andreas S. Stordal Hans A. Karlsen Geir Nævdal Hans J. Skaug Brice Vallès 《Computational Geosciences》2011,15(2):293-305
The nonlinear filtering problem occurs in many scientific areas. Sequential Monte Carlo solutions with the correct asymptotic
behavior such as particle filters exist, but they are computationally too expensive when working with high-dimensional systems.
The ensemble Kalman filter (EnKF) is a more robust method that has shown promising results with a small sample size, but the
samples are not guaranteed to come from the true posterior distribution. By approximating the model error with a Gaussian
distribution, one may represent the posterior distribution as a sum of Gaussian kernels. The resulting Gaussian mixture filter
has the advantage of both a local Kalman type correction and the weighting/resampling step of a particle filter. The Gaussian
mixture approximation relies on a bandwidth parameter which often has to be kept quite large in order to avoid a weight collapse
in high dimensions. As a result, the Kalman correction is too large to capture highly non-Gaussian posterior distributions.
In this paper, we have extended the Gaussian mixture filter (Hoteit et al., Mon Weather Rev 136:317–334, 2008) and also made the connection to particle filters more transparent. In particular, we introduce a tuning parameter for the
importance weights. In the last part of the paper, we have performed a simulation experiment with the Lorenz40 model where
our method has been compared to the EnKF and a full implementation of a particle filter. The results clearly indicate that
the new method has advantages compared to the standard EnKF. 相似文献
14.
对于大面积浅层风化土边坡,当下部含有浅水位或不透水基岩层时大降雨将导致下部气体被封闭。随着湿润峰的下移,气压不断增大。封闭气压力不仅降低了雨水在边坡土体的入渗率,而且对边坡的稳定有显著影响。通过分析封闭气压力的形成和相关理论,提出取 大小的气压力头来研究边坡的稳定性（Hc为一水头值,与土体孔隙尺寸分布有关; hd为土体进气值水头）。结合非饱和土的Mohr-Coulomb破坏准则和极限平衡法,将封闭气压力引入到边坡的稳定分析中,建立了考虑气压力影响下的稳定分析模型。与传统的不考虑气压力的稳定分析方法作对比,提出了气压力影响率概念。研究表明,封闭气压力显著降低了边坡的安全系数,传统的无限边坡稳定计算方法偏于危险。研究结果对无限边坡的强降雨安全预报具有较好的指导作用。 相似文献
15.
非线性滤波方法与陆面数据同化 总被引：8，自引：4，他引：4
陆面数据同化研究近几年成为地球科学研究的新兴领域,其中以非线性滤波为代表的数据同化方法发展迅速并得到了广泛应用。在贝叶斯理论框架内,从递推贝叶斯估计理论的角度系统地分析了扩展卡尔曼滤波、无迹卡尔曼滤波、集合卡尔曼滤波、SIR粒子滤波等非线性滤波方法的异同;针对应用比较广泛的集合卡尔曼滤波和SIR粒子滤波应用中存在的问题,论述了几种提高滤波性能的实用方法,如协方差矩阵的Localization方法、协方差矩阵的Inflation方法、双集合卡尔曼滤波方法、扰动集合、扰动大气驱动和模型参数、平方根集合卡尔曼滤波以及粒子滤波算法的改进等。最后总结讨论了各种非线性滤波方法应用中的特点、难点以及各种算法在陆面数据同化中的应用前景和发展方向。 相似文献
16.
An approximate analytical solution for measuring air permeability of asphalt samples partially saturated with water 总被引：1，自引：1，他引：0
In coastal areas, abnormally high pressure may be caused by the tide-induced water table variation under extensive pavements,
particularly during rainfall. To simulate the rainfall infiltration effects on the air permeability of asphalt pavements in
coastal area, column-shaped asphalt sample was fixed in the upper part of a steel cylinder with its upper surface saturated
with ponding water (depth < 5 mm) and open to the atmosphere. The cylinder’s lower part formed an air chamber. The chamber
was pressurized and then the air therein was released naturally through the sample. The pressure variation with time in the
chamber was recorded for analysis. Based on the Green–Ampt piston model for the surface water infiltration, an approximate
analytical solution was derived to describe the pressure–time relationship in the chamber. A new parameter called the escape
pressure was introduced to describe the air pressure needed for the chamber air to break through the capillary pressure induced
by the ponding water. The analytical solution gave good estimations of both the escape pressures and the harmonic averages
of the permeabilities of the wet and dry parts of 14 samples in the sense that excellent fittings were obtained between the
observed and predicted air pressures in the air chamber. The estimated escape pressure ranges from 0.0 to 1.74 kPa. The harmonic
average of the permeabilities of the wet and dry parts is 5–94% of the dry sample’s permeability. 相似文献
17.
In a previous paper, we developed a theoretical basis for parameterization of reservoir model parameters based on truncated
singular value decomposition (SVD) of the dimensionless sensitivity matrix. Two gradient-based algorithms based on truncated
SVD were developed for history matching. In general, the best of these “SVD” algorithms requires on the order of 1/2 the number
of equivalent reservoir simulation runs that are required by the limited memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm.
In this work, we show that when combining SVD parameterization with the randomized maximum likelihood method, we can achieve
significant additional computational savings by history matching all models simultaneously using a SVD parameterization based
on a particular sensitivity matrix at each iteration. We present two new algorithms based on this idea, one which relies only
on updating the SVD parameterization at each iteration and one which combines an inner iteration based on an adjoint gradient
where during the inner iteration the truncated SVD parameterization does not vary. Results generated with our algorithms are
compared with results obtained from the ensemble Kalman filter (EnKF). Finally, we show that by combining EnKF with the SVD-algorithm,
we can improve the reliability of EnKF estimates. 相似文献
18.
Data assimilation in reservoir modeling often involves model variables that are multimodal, such as porosity and permeability. Well established data assimilation methods such as ensemble Kalman filter and ensemble smoother approaches, are based on Gaussian assumptions that are not applicable to multimodal random variables. The selection ensemble smoother is introduced as an alternative to traditional ensemble methods. In the proposed method, the prior distribution of the model variables, for example the porosity field, is a selection-Gaussian distribution, which allows modeling of the multimodal behavior of the posterior ensemble. The proposed approach is applied for validation on a two-dimensional synthetic channelized reservoir. In the application, an unknown reservoir model of porosity and permeability is estimated from the measured data. Seismic and production data are assumed to be repeatedly measured in time and the reservoir model is updated every time new data are assimilated. The example shows that the selection ensemble Kalman model improves the characterisation of the bimodality of the model parameters compared to the results of the ensemble smoother.
相似文献19.
Continuous Facies Updating Using the Ensemble Kalman Filter and the Level Set Method 总被引：1，自引：1，他引：0
We present a methodology based on the ensemble Kalman filter (EnKF) and the level set method for the continuous model updating of geological facies with respect to production data. Geological facies are modeled using an implicit surface representation and conditioned to production data using the ensemble Kalman filter. The methodology is based on Gaussian random fields used to deform the facies boundaries. The Gaussian random fields are used as the model parameter vector to be updated sequentially within the EnKF when new measurements are available. We show the successful application of the methodology to two synthetic reservoir models. 相似文献
20.
Waterflooding using closed-loop control 总被引：2，自引：0，他引：2
To fully exploit the possibilities of “smart” wells containing both measurement and control equipment, one can envision a
system where the measurements are used for frequent updating of a reservoir model, and an optimal control strategy is computed
based on this continuously updated model. We developed such a closed-loop control approach using an ensemble Kalman filter
to obtain frequent updates of a reservoir model. Based on the most recent update of the reservoir model, the optimal control
strategy is computed with the aid of an adjoint formulation. The objective is to maximize the economic value over the life
of the reservoir. We demonstrate the methodology on a simple waterflooding example using one injector and one producer, each
equipped with several individually controllable inflow control valves (ICVs). The parameters (permeabilities) and dynamic
states (pressures and saturations) of the reservoir model are updated from pressure measurements in the wells. The control
of the ICVs is rate-constrained, but the methodology is also applicable to a pressure-constrained situation. Furthermore,
the methodology is not restricted to use with “smart” wells with down-hole control, but could also be used for flooding control
with conventional wells, provided the wells are equipped with controllable chokes and with sensors for measurement of (wellhead
or down hole) pressures and total flow rates. As the ensemble Kalman filter is a Monte Carlo approach, the final results will
vary for each run. We studied the robustness of the methodology, starting from different initial ensembles. Moreover, we made
a comparison of a case with low measurement noise to one with significantly higher measurement noise. In all examples considered,
the resulting ultimate recovery was significantly higher than for the case of waterflooding using conventional wells. Furthermore,
the results obtained using closed-loop control, starting from an unknown permeability field, were almost as good as those
obtained assuming a priori knowledge of the permeability field. 相似文献