OpenDA-NEMO framework for ocean data assimilation

Data assimilation methods provide a means to handle the modeling errors and uncertainties in sophisticated ocean models. In this study, we have created an OpenDA-NEMO framework unlocking the data assimilation tools available in OpenDA for use with NEMO models. This includes data assimilation methods, automatic parallelization, and a recently implemented automatic localization algorithm that removes spurious correlations in the model based on uncertainties in the computed Kalman gain matrix. We have set up a twin experiment where we assimilate sea surface height (SSH) satellite measurements. From the experiments, we can conclude that the OpenDA-NEMO framework performs as expected and that the automatic localization significantly improves the performance of the data assimilation algorithm by successfully removing spurious correlations. Based on these results, it looks promising to extend the framework with new kinds of observations and work on improving the computational speed of the automatic localization technique such that it becomes feasible to include large number of observations.     

Model-based framework for multi-axial real-time hybrid simulation testing

Real-time hybrid simulation is an efficient and cost-effective dynamic testing technique for performance evaluation of structural systems subjected to earthquake loading with rate-dependent behavior. A loading assembly with multiple actuators is required to impose realistic boundary conditions on physical specimens. However, such a testing system is expected to exhibit significant dynamic coupling of the actuators and suffer from time lags that are associated with the dynamics of the servo-hydraulic system, as well as control-structure interaction (CSI). One approach to reducing experimental errors considers a multi-input, multi-output (MIMO) controller design, yielding accurate reference tracking and noise rejection. In this paper, a framework for multi-axial real-time hybrid simulation (maRTHS) testing is presented. The methodology employs a real-time feedback-feedforward controller for multiple actuators commanded in Cartesian coordinates. Kinematic transformations between actuator space and Cartesian space are derived for all six-degrees-offreedom of the moving platform. Then, a frequency domain identification technique is used to develop an accurate MIMO transfer function of the system. Further, a Cartesian-domain model-based feedforward-feedback controller is implemented for time lag compensation and to increase the robustness of the reference tracking for given model uncertainty. The framework is implemented using the 1/5th-scale Load and Boundary Condition Box (LBCB) located at the University of Illinois at Urbana- Champaign. To demonstrate the efficacy of the proposed methodology, a single-story frame subjected to earthquake loading is tested. One of the columns in the frame is represented physically in the laboratory as a cantilevered steel column. For realtime execution, the numerical substructure, kinematic transformations, and controllers are implemented on a digital signal processor. Results show excellent performance of the maRTHS framework when six-degrees-of-freedom are controlled at the interface between substructures.     

A model-order reduction framework for hybrid simulation based on component-mode synthesis

Testing of stiff physical substructures (PSs) still poses major technical issues that prevent from adopting hybrid simulation (HS) as a standard structural testing method. Firstly, elastic deformation of reaction frames, as well as the limited resolution of displacement transducers, deteriorate displacement control accuracy. Secondly, as a consequence of control errors, small perturbations of actuator displacements entail large restoring force oscillations that spuriously excite the higher eigenmodes of the hybrid model. For this reason, in the current practice, force-controlled hydraulic jacks handle vertical degrees of freedom, which are typically associated with stiff axially loaded members and excluded from the time integration loop. Vertical forces are either kept constant or adjusted during the experiment based on simplified redistribution rules. Besides deterioration of displacement control accuracy, stiff PSs naturally increase the frequency bandwidth of the hybrid model, whose higher eigenfrequencies (divided by the testing time scale) may fall outside the frequency bandwidth of the actuation system, thus destabilizing the HS. This is a collateral issue to which, in the authors' knowledge, no sufficient attention as been dedicated yet, and this paper tries to address it. From this standpoint, we propose component-mode synthesis as a rigorous approach for deriving reduced-order physical and numerical substructure mass and stiffness matrices that minimize the frequency bandwidth of the hybrid model. The proposed methodology allowed for performing HSs of a load-bearing unreinforced masonry structure including both horizontal and vertical degrees of freedom with a standard three-actuator setup used for cyclic testing.     

基于特征结构配置的结构鲁棒控制算法及仿真

本文提出了结构系统的鲁棒主动控制算法，其目的是求取状态反馈控制律，在闭环系统具有希望特征值前提下，使得闭环系统特征值关于参数摄动具有最小的灵敏度。基于状态反馈特征结构配置参数化方法和矩阵特征值灵敏度分析理论，该问题转化为含有约束条件的优化问题，并给出了求解该鲁棒控制问题的算法。该方法给出了特征值灵敏度函数的参数化表示，且直接基于结构系统矩阵，故便于工程应用。三自由度层间剪切型结构地震作用下的仿真分析，表明所提方法的有效性。     

A multidimensional scaling approach to enforce reproduction of transition probabilities in truncated plurigaussian simulation

Truncated plurigaussian (TPG) simulation is a flexible method for simulating rock types in deposits with complicated ordering structures. The truncation of a multivariate Gaussian distribution controls the proportions and ordering of rock types in the simulation while the variogram for each Gaussian variable controls rock type continuity. The determination of a truncation procedure for complicated geological environments is not trivial. A method for determining the truncation and fitting variograms applicable to any number of rock types and multivariate Gaussian distribution is developed here to address this problem. Multidimensional scaling is applied to place dissimilar categories far apart and similar categories close together. The multivariate space is then mapped using a Voronoi decomposition and rotated to optimize variogram reproduction. A case study simulating geologic layers at a large mineral deposit demonstrates the potential of this method and compares the results with sequential indicator simulation (SIS). Input proportion and transition probability reproduction with TPG is demonstrated to be better than SIS. Variogram reproduction is comparable for both techniques.     

Software framework for distributed experimental–computational simulation of structural systems

Supported by the recent advancement of experimental test methods, numerical simulation, and high‐speed communication networks, it is possible to distribute geographically the testing of structural systems using hybrid experimental–computational simulation. One of the barriers for this advanced testing is the lack of flexible software for hybrid simulation using heterogeneous experimental equipment. To address this need, an object‐oriented software framework is designed, developed, implemented, and demonstrated for distributed experimental–computational simulation of structural systems. The software computes the imposed displacements for a range of test methods and co‐ordinates the control of local and distributed configurations of experimental equipment. The object‐oriented design of the software promotes the sharing of modules for experimental equipment, test set‐ups, simulation models, and test methods. The communication model for distributed hybrid testing is similar to that used for parallel computing to solve structural simulation problems. As a demonstration, a distributed pseudodynamic test was conducted using a client–server approach, in which the server program controlled the test equipment in Japan and the client program performed the computational simulation in the United States. The distributed hybrid simulation showed that the software framework is flexible and reliable. Copyright © 2005 John Wiley & Sons, Ltd.     

1D inversion of DC resistivity data using a quality-based truncated SVD

Many DC resistivity inversion schemes use a combination of standard iterative least-squares and truncated singular value decomposition (SVD) to optimize the solution to the inverse problem. However, until quite recently, the truncation was done arbitrarily or by a trial-and-error procedure, due to the lack of workable guidance criteria for discarding small singular values. In this paper we present an inversion scheme which adopts a truncation criterion based on the optimization of the total model variance. This consists of two terms: (i) the term associated with the variance of statistically significant principal components, i.e. the standard model estimate variance, and (ii) the term associated with statistically insignificant principal components of the solution, i.e. the variance of the bias term. As an initial model for the start of iterations, we use a multilayered homogeneous half-space whose layer thicknesses increase logarithmically with depth to take into account the decrease of the resolution of the DC resistivity technique with depth. The present inversion scheme has been tested on synthetic and field data. The results of the tests show that the procedure works well and the convergence process is stable even in the most complicated cases. The fact that the truncation level in the SVD is determined intrinsically in the course of inversion proves to be a major advantage over other inversion schemes where it is set by the user.     

A statistical framework of data fusion for spatial prediction of categorical variables

With rapid advances of geospatial technologies, the amount of spatial data has been increasing exponentially over the past few decades. Usually collected by diverse source providers, the available spatial data tend to be fragmented by a large variety of data heterogeneities, which highlights the need of sound methods capable of efficiently fusing the diverse and incompatible spatial information. Within the context of spatial prediction of categorical variables, this paper describes a statistical framework for integrating and drawing inferences from a collection of spatially correlated variables while accounting for data heterogeneities and complex spatial dependencies. In this framework, we discuss the spatial prediction of categorical variables in the paradigm of latent random fields, and represent each spatial variable via spatial covariance functions, which define two-point similarities or dependencies of spatially correlated variables. The representation of spatial covariance functions derived from different spatial variables is independent of heterogeneous characteristics and can be combined in a straightforward fashion. Therefore it provides a unified and flexible representation of heterogeneous spatial variables in spatial analysis while accounting for complex spatial dependencies. We show that in the spatial prediction of categorical variables, the sought-after class occurrence probability at a target location can be formulated as a multinomial logistic function of spatial covariances of spatial variables between the target and sampled locations. Group least absolute shrinkage and selection operator is adopted for parameter estimation, which prevents the model from over-fitting, and simultaneously selects an optimal subset of important information (variables). Synthetic and real case studies are provided to illustrate the introduced concepts, and showcase the advantages of the proposed statistical framework.     

Missing precipitation data estimation using optimal proximity metric-based imputation,nearest-neighbour classification and cluster-based interpolation methods

Abstract

New optimal proximity-based imputation, K-nearest neighbour (K-NN) classification and K-means clustering methods are proposed and developed for estimation of missing daily precipitation records. Mathematical programming formulations are developed to optimize the weighting, classification and clustering schemes used in these methods. Ten different binary and real-valued distance metrics are used as proximity measures. Two climatic regions, Kentucky and Florida, (temperate and tropical) in the USA, with different gauge density and network structure, are used as case studies to evaluate the new methods. A comprehensive exercise is undertaken to compare the performances of the new methods with those of several deterministic and stochastic spatial interpolation methods. The results from these comparisons indicate that the proposed methods performed better than existing methods. Use of optimal proximity metrics as weights, spatial clustering of observation sites and classification of precipitation data resulted in improvement of missing data estimates.

Editor D. Koutsoyiannis; Associate editor C. Onof     

Inference for the truncated exponential distribution

The constructed estimator is introduced for the right truncation point of the truncated exponential distribution. The new estimator is most efficient in important ranges of truncation points for finite sample sizes. The introduced inverse mean squared error clearly indicates the good behaviour of the new estimator. The estimation of the scaling parameter is considered in all discussions and computations. The methods and models of the extreme value theory are not appropriate to estimate the truncation point because they work only in the case of very large sample sizes. Furthermore, a procedure for a first goodness-of-fit test is introduced. All this has been researched by extensive Monte Carlo simulations for different truncation points and sample sizes. Finally, the new inference methods are applied at the end for the random distribution of wildfire sizes and earthquake magnitudes.     

A data assimilation approach for groundwater parameter estimation under Bayesian maximum entropy framework

Spatial heterogeneity in groundwater system introduces significant challenges in groundwater modeling and parameter calibration. In order to mitigate the modeling uncertainty, data assiilation...     

平面波域反数据处理压制多次波方法研究

在地震勘探领域,尤其是海洋地震勘探中,多次波一直是影响地震处理与解释的主要因素之一.本文基于x-t域的反馈模型及多次波衰减的理论,详尽推导了x-t域反数据处理的方法;基于平面波域多次波的产生机制,借鉴x-t域反数据处理的方法,推导出在平面波域进行反数据处理的原理.文中给出一个有限差分的模拟数据进行测试,处理结果表明,本方法可以有效地衰减表层相关多次波,提高地震数据分析的精确性,在保护了一次波能量的同时,可以更加有效快捷地去除多次波.     

Obtaining basic simulation data for a heterogeneous field with stratified marine soils

Thinly stratified sedimentary deposits in a heterogeneous field were investigated to obtain basic physical data for the simulation of water flow. A procedure is described which translates a thinly stratified soil profile into a number of functional layers using functional hydrological properties. A functional layer is defined as a combination of one or more soil horizons and should (i) be recognizable during a soil survey using an auger and (ii) show significantly different functional hydrological properties when compared with another functional layer. This procedure gave three easily recognizable functional layers. Sets of hydrological characteristics of these three functional layers were obtained by physical measurements of the soil and by estimation, using textural data for classification into a standard Dutch series. The performance of several combinations of these sets was tested by comparing simulated and measured soil matric potentials for seven plots during one year. The best simulation results were obtained if measured soil hydraulic characteristics were used for relatively homogeneous functional layers and if the soil hydraulic characteristics were estimated at each location for the most heterogeneous layer.     

多极子随钻声波测井波场模拟与采集模式分析

应用三维非均匀交错网格有限差分程序模拟了多极子随钻声波测井的波场,考察了硬地层中单极子随钻声波测井响应及软地层中声源频率对多极子随钻声波测井的影响;计算了接收阵列波形的时域相干谱及频散特征,结果表明随钻测井仪器居中时,在硬地层中可以直接利用单极子声系得到地层横波速度,但地层纵波受到钻铤波的极大干扰.而在软地层中可以利用单极子声系直接测得地层纵波速度,但地层横波速度则需要使用工作在较低频率下的四极子随钻声波仪来测得,此时测井频段又会落入钻井噪声频带而受到影响.为此本文提出数值模拟手段结合物理实验的方法得到钻铤波的影响,在实际测井中通过信号处理方法而不是用在仪器本体上刻槽的方式来更完全地消除钻铤的影响.为了避免钻井噪声的影响,本文提出六极子随钻声波仪更适合软地层的横波测量.本文用数值实验结果来说明这些方法的可行性.     

A general Bayesian framework for calibrating and evaluating stochastic models of annual multi-site hydrological data

Multi-site simulation of hydrological data are required for drought risk assessment of large multi-reservoir water supply systems. In this paper, a general Bayesian framework is presented for the calibration and evaluation of multi-site hydrological data at annual timescales. Models included within this framework are the hidden Markov model (HMM) and the widely used lag-1 autoregressive (AR(1)) model. These models are extended by the inclusion of a Box–Cox transformation and a spatial correlation function in a multi-site setting. Parameter uncertainty is evaluated using Markov chain Monte Carlo techniques. Models are evaluated by their ability to reproduce a range of important extreme statistics and compared using Bayesian model selection techniques which evaluate model probabilities. The case study, using multi-site annual rainfall data situated within catchments which contribute to Sydney’s main water supply, provided the following results: Firstly, in terms of model probabilities and diagnostics, the inclusion of the Box–Cox transformation was preferred. Secondly the AR(1) and HMM performed similarly, while some other proposed AR(1)/HMM models with regionally pooled parameters had greater posterior probability than these two models. The practical significance of parameter and model uncertainty was illustrated using a case study involving drought security analysis for urban water supply. It was shown that ignoring parameter uncertainty resulted in a significant overestimate of reservoir yield and an underestimation of system vulnerability to severe drought.     

Petrophysics and mineral exploration: a workflow for data analysis and a new interpretation framework

As mineral exploration seeks deeper targets, there will be a greater reliance on geophysical data and a better understanding of the geological meaning of the responses will be required, and this must be achieved with less geological control from drilling. Also, exploring based on the mineral system concept requires particular understanding of geophysical responses associated with altered rocks. Where petrophysical datasets of adequate sample size and measurement quality are available, physical properties show complex variations, reflecting the combined effects of various geological processes. Large datasets, analysed as populations, are required to understand the variations. We recommend the display of petrophysical data as frequency histograms because the nature of the data distribution is easily seen with this form of display. A petrophysical dataset commonly contains a combination of overlapping sub-populations, influenced by different geological factors. To understand the geological controls on physical properties in hard rock environments, it is necessary to analyse the petrophysical data not only in terms of the properties of different rock types. It is also necessary to consider the effects of processes such as alteration, weathering, metamorphism and strain, and variables such as porosity and stratigraphy. To address this complexity requires that much more supporting geological information be acquired than in current practice. The widespread availability of field portable instruments means quantitative geochemical and mineralogical data can now be readily acquired, making it unnecessary to rely primarily on categorical rock classification schemes. The petrophysical data can be combined with geochemical, petrological and mineralogical data to derive explanations for observed physical property variations based not only on rigorous rock classification methods, but also in combination with quantitative estimates of alteration and weathering. To understand how geological processes will affect different physical properties, it is useful to define three end-member forms of behaviour. Bulk behaviour depends on the physical properties of the dominant mineral components. Density and, to a lesser extent, seismic velocity show such behaviour. Grain and texture behaviour occur when minor components of the rock are the dominate controls on its physical properties. Grain size and shape control grain properties, and for texture properties the relative positions of these grains are also important. Magnetic and electrical properties behave in this fashion. Thinking in terms of how geological processes change the key characteristics of the major and minor mineralogical components allows the resulting changes in physical properties to be understood and anticipated.     

Equilibrium trading of climate and weather risk and numerical simulation in a Markovian framework

We consider financial markets with agents exposed to external sources of risk caused, for example, by short-term climate events such as the South Pacific sea surface temperature anomalies widely known by the name El Nino. Since such risks cannot be hedged through investments on the capital market alone, we face a typical example of an incomplete financial market. In order to make this risk tradable, we use a financial market model in which an additional insurance asset provides another possibility of investment besides the usual capital market. Given one of the many possible market prices of risk, each agent can maximize his individual exponential utility from his income obtained from trading in the capital market, the additional security, and his risk-exposure function. Under the equilibrium market-clearing condition for the insurance security the market price of risk is uniquely determined by a backward stochastic differential equation. We translate these stochastic equations via the Feynman–Kac formalism into semi-linear parabolic partial differential equations. Numerical schemes are available by which these semilinear pde can be simulated. We choose two simple qualitatively interesting models to describe sea surface temperature, and with an ENSO risk exposed fisher and farmer and a climate risk neutral bank three model agents with simple risk exposure functions. By simulating the expected appreciation price of risk trading, the optimal utility of the agents as a function of temperature, and their optimal investment into the risk trading security we obtain first insight into the dynamics of such a market in simple situations.

DInSAR technique for three-dimensional coastal spit simulation from radarsat-1 fine mode data

This work presents a new approach to 3D spit simulation using differential synthetic aperture interferometry (DInSAR). In doing so, conventional DInSAR procedures are implemented to three repeat passes of RADARSAT-1 SAR fine mode data (F1). Further, a new application of using fuzzy B-spline algorithm is implemented with phase unwrapping technique. The study shows that the performance of DInSAR method using fuzzy B-spline is better than the DInSAR technique, which is validated by the coefficient of determination r ² of 0.96, probability p of 0.002, and accuracy (RMSE) of ± 0.034 m, with 90% confidence intervals. In conclusion, integration of fuzzy B-spline with phase unwrapping produces an accurate 3D coastal geomorphology reconstruction.     

Numerical simulation and development of data inversion in borehole ultrasonic imaging

The major function of underground nuclear waste disposal is to avoid the migration of radionucleides towards the biosphere during their active period. This function can be deteriorated by the EDZ (excavated damaged zone) around the excavation. The EDZ analysis is therefore crucial in the performance assessment of the storage. The paper deals with the determination of the EDZ around a nuclear waste storage cavity using borehole ultrasonic imaging (azimuthal tomography). Indeed, before processing experimental data obtained with this tool, it is necessary to establish that data can be satisfactorily inverted. This analysis is based on a method that is able to sound and image the rock mass velocity field. The velocity field is numerically simulated (3D geomechanical modeling) based on an assumption on the relationship between stress and velocity fields. In order to evaluate a radial velocity profile starting from inter-sensor distance and their corresponding traveltimes, different ray tracing algorithms are tested using synthetic data. These tests led to a simple and fast approach (implemented in a Mathematica package) to process a large quantity of data.