Least Square Data Assimilation for Identification of the Point Source Emissions

The identification of single and multiple-point emission sources from limited number of atmospheric concentration measurements is addressed using least square data assimilation technique. During the process, a new two-step algorithm is proposed for optimization, free from initialization and filtering singular regions in a natural way. Source intensities are expressed in terms of their locations reducing the degree of freedom of unknowns to be estimated. In addition, a strategy is suggested for reducing the computational time associated with the multiple-point source identification. The methodology is evaluated with the synthetic, pseudo-real and noisy set of measurements for two and three simultaneous point emissions. With the synthetic data, algorithm estimates the source parameters exactly same as the prescribed in all the cases. With the pseudo-real data, two and three point release locations are retrieved with an average error of 17 m and intensities are estimated on an average within a factor of 2. Finally, the advantages and limitations of the proposed methodology are discussed.     

Assimilation of Paleoseismic Data for Earthquake Simulation

— Simulation of the complete earthquake generation process requires assimilation of observational data on long-term behavior of faults over multiple rupture cycles. Paleoseismology, the study of pre-instrumental earthquakes from geological field investigations, provides the only observations of long-term fault behavior. Paleoseismic data present challenges for assimilation into models because data sets are small, sparse, analog, and contain qualitative uncertainties. Observations can be categorized as primary measurements from field data, or non-primary extrapolations or interpretations of primary data. Assimilation of non-primary data could yield biased simulation results. We present a primary data set for the San Andreas and Imperial faults for comparison with non-primary data in existing databases, and propose standard formats and structure for paleoseismic data assimilation into numerical simulation models .     

A Semi-Evolutive Partially Local Filter for Data Assimilation

The singular evolutive extended Kalman (SEEK) filter has been proposed recently by Pham et al. (1997) for data assimilation into numerical oceanic models. This filter has been applied in different realistic ocean frameworks and has provided satisfactory results ( Pham et al., 1997; Verron et al., 1998). However, the SEEK filter remains expensive in real operational assimilation. To reduce cost and obtain a better representativity, we introduce the idea ‘local correction basis'. Such basis however cannot be made to evolve according to the model without destroying its locality property. Therefore we shall keep this basis fixed and we augment it by a few global basis vectors which evolve. The resulting semi-evolutive partially local filter is much less costly to implement than the SEEK filter and yet can yield better results. In the first application, validation twin experiments are conducted in a realistic setting of the OPA model over the tropical Pacific Ocean.     

Full-Wave Seismic Data Assimilation: Theoretical Background and Recent Advances

The seismological inverse problem has much in common with the data assimilation problem found in meteorology and oceanography. Using the data assimilation methodology, I will formulate the seismological inverse problem for estimating seismic source and Earth structure parameters in the form of weak-constraint generalized inverse, in which the seismic wave equation and the associated initial and boundary conditions are allowed to contain errors. The resulting Euler?CLagrange equations are closely related to the adjoint method and the scattering-integral method, which have been successfully applied in full-3D, full-wave seismic tomography and earthquake source parameter inversions. I will review some recent applications of the full-wave methodology in seismic tomography and seismic source parameter inversions and discuss some challenging issues related to the computational implementation and the effective exploitation of seismic waveform data.     

三维CT图象精确重建的源点轨迹

本文从三维锥束精度重建的完全条件出发，着重介绍了几种满足完全条件的源点轨迹及其参数沦描述，给出并证明了控制轨迹尺寸的相关变量的取值范围。     

Decision Support Modeling: Data Assimilation,Uncertainty Quantification,and Strategic Abstraction

We present a framework for design and deployment of decision support modeling based on metrics which have their roots in the scientific method. Application of these metrics to decision support modeling requires recognition of the importance of data assimilation and predictive uncertainty quantification in this type of modeling. The difficulties of implementing these procedures depend on the relationship between data that is available for assimilation and the nature of the prediction(s) that a decision support model is required to make. Three different data/prediction contexts are identified. Unfortunately, groundwater modeling is generally aligned with the most difficult of these. It is suggested that these difficulties can generally be ameliorated through appropriate model design. This design requires strategic abstraction of parameters and processes in a way that is optimal for the making of one particular prediction but is not necessarily optimal for the making of another. It is further suggested that the focus of decision support modeling should be on the ability of a model to provide receptacles for decision-pertinent information rather than on its purported ability to simulate environmental processes. While models are compromised in both of these roles, this view makes it clear that simulation should serve data assimilation and not the other way around. Data assimilation enables the uncertainties of decision-critical model predictions to be quantified and maybe reduced. Decision support modeling requires this.     

Benefits and Cautions in Data Assimilation Strategies: An Example of Modeling Groundwater Recharge

Assimilating recent observations improves model outcomes for real-time assessments of groundwater processes. This is demonstrated in estimating time-varying recharge to a shallow fractured-rock aquifer in response to precipitation. Results from estimating the time-varying water-table altitude (h) and recharge, and their error covariances, are compared for forecasting, filtering, and fixed-lag smoothing (FLS), which are implemented using the Kalman Filter as applied to a data-driven, mechanistic model of recharge. Forecasting uses past observations to predict future states and is the current paradigm in most groundwater modeling investigations; filtering assimilates observations up to the current time to estimate current states; and FLS estimates states following a time lag over which additional observations are collected. Results for forecasting yield a large error covariance relative to the magnitude of the expected recharge. With assimilating recent observations of h, filtering and FLS produce estimates of recharge that better represent time-varying observations of h and reduce uncertainty in comparison to forecasting. Although model outcomes from applying data assimilation through filtering or FLS reduce model uncertainty, they are not necessarily mass conservative, whereas forecasting outcomes are mass conservative. Mass conservative outcomes from forecasting are not necessarily more accurate, because process errors are inherent in any model. Improvements in estimating real-time groundwater conditions that better represent observations need to be weighed for the model application against outcomes with inherent process deficiencies. Results from data assimilation strategies discussed in this investigation are anticipated to be relevant to other groundwater processes models where system states are sensitive to system inputs.     

Relationship of Tsunami Intensity to Source Earthquake Magnitude as Retrieved from Historical Data

Operational prediction of near-field tsunamis in all existing Tsunami Warning Systems (TWSs) is based on fast determination of the position and size of submarine earthquakes. Exceedance of earthquake magnitude above some established threshold value, which can vary over different tsunamigenic zones, results in issuing a warning signal. Usually, a warning message has several (from 2 to 5) grades reflecting the degree of tsunami danger and sometimes contains expected wave heights at the coast. Current operational methodology is based on two main assumptions: (1) submarine earthquakes above some threshold magnitude can generate dangerous tsunamis and (2) the height of a resultant tsunami is, in general, proportional to the earthquake magnitude. While both assumptions are physically reasonable and generally correct, statistics of issued warnings are far from being satisfactory. For the last 55 years, up to 75% of warnings for regional tsunamis have turned out to be false, while each TWS has had at least a few cases of missing dangerous tsunamis. This paper presents the results of investigating the actual dependence of tsunami intensity on earthquake magnitude as it can be retrieved from historical observations and discusses the degree of correspondence of the above assumptions to real observations. Tsunami intensity, based on the Soloviev-Imamura scale is used as a measure of tsunami “size”. Its correlation with the M _s and M _w magnitudes is investigated based on historical data available for the instrumental period of observations (from 1900 to present).     

Assimilation of Doppler Weather Radar Data in WRF Model for Simulation of Tropical Cyclone Aila

For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data.     

电子加速器X射线源能谱整形理论与模拟研究

利用能谱整形技术产生的X射线优化能谱可减小伪影误差,提高无损检测材料识别精度。如何利用能谱整形技术产生指定的优化能谱是要解决的关键问题。为解决这一问题,对能谱整形基本理论进行了研究,设定了能谱整形的目标,利用蒙特卡罗方法模拟计算出电子加速器出射X射线能谱,对能谱和整形材料的线性衰减系数进行拟合,计算了能谱整形所需要的相关参数并确定能谱整形方法,运用蒙特卡罗方法模拟验证了所采用的能谱整形方法的合理性。     

Calibration/Data Assimilation Approach for Integrating GRACE Data into the WaterGAP Global Hydrology Model (WGHM) Using an Ensemble Kalman Filter: First Results

We introduce a new ensemble-based Kalman filter approach to assimilate GRACE satellite gravity data into the WaterGAP Global Hydrology Model. The approach (1) enables the use of the spatial resolution provided by GRACE by including the satellite observations as a gridded data product, (2) accounts for the complex spatial GRACE error correlation pattern by rigorous error propagation from the monthly GRACE solutions, and (3) allows us to integrate model parameter calibration and data assimilation within a unified framework. We investigate the formal contribution of GRACE observations to the Kalman filter update by analysis of the Kalman gain matrix. We then present first model runs, calibrated via data assimilation, for two different experiments: the first one assimilates GRACE basin averages of total water storage and the second one introduces gridded GRACE data at \(5^\circ\) resolution into the assimilation. We finally validate the assimilated model by running it in free mode (i.e., without adding any further GRACE information) for a period of 3 years following the assimilation phase and comparing the results to the GRACE observations available for this period.     

一种新的卫星热红外遥感信息数据源:EOS/MODIS数据

概略介绍了四川省地震局近年来所开展的卫星遥感热红外辐射信息用于地震监测预报的研究工作及其进展；对目前地震系统卫星遥感热红外辐射信息研究工作所使用的数据源进行了比较；着重介绍了一种新的卫星热红外遥感信息数据源——搭载于美国地球观测系统(EOS)的Terra和Aqua极轨卫星上的中分辨率成像光谱仪(MODIS)这一唯一进行直接广播的对地观测仪器的基本参数及数据特性。对MODIS数据与目前正在使用的AVHRR数据进行对比分析后认为，EOS／MODIS数据的产生，将进一步推动利用卫星热红外辐射资料研究强震前的热异常场的工作，并可望在我国地震监测预报实践中得到应用。     

Reconstruction of the Water Table from Self-Potential Data: A Bayesian Approach

Ground water flow associated with pumping and injection tests generates self-potential signals that can be measured at the ground surface and used to estimate the pattern of ground water flow at depth. We propose an inversion of the self-potential signals that accounts for the heterogeneous nature of the aquifer and a relationship between the electrical resistivity and the streaming current coupling coefficient. We recast the inversion of the self-potential data into a Bayesian framework. Synthetic tests are performed showing the advantage in using self-potential signals in addition to in situ measurements of the potentiometric levels to reconstruct the shape of the water table. This methodology is applied to a new data set from a series of coordinated hydraulic tomography, self-potential, and electrical resistivity tomography experiments performed at the Boise Hydrogeophysical Research Site, Idaho. In particular, we examine one of the dipole hydraulic tests and its reciprocal to show the sensitivity of the self-potential signals to variations of the potentiometric levels under steady-state conditions. However, because of the high pumping rate, the response was also influenced by the Reynolds number , especially near the pumping well for a given test. Ground water flow in the inertial laminar flow regime is responsible for nonlinearity that is not yet accounted for in self-potential tomography. Numerical modeling addresses the sensitivity of the self-potential response to this problem.     

一种工业CT点云数据的NURBS曲面重构算法

虽然CT技术已经发展得相当成熟,但保证检测数据重构的精度还存在较大困难。本文将工业CT图像转化为三维散乱点云数据,研究数据预处理算法;对处理后数据进行NURBS曲面插值,完成了对NURBS曲面的重构。以某工件工业CT图像为例,通过VG软件转化为点云数据,利用VC++和OpenGL编程实现数据预处理算法和NURBS曲面拟合,并用实例验证了该曲面造型方法的正确性。     

Fuzzy Logic Determination of Lithologies from Well Log Data: Application to the KTB Project Data set (Germany)

Fuzzy logic has been used for lithology prediction with remarkable success. Several techniques such as fuzzy clustering or linguistic reasoning have proven to be useful for lithofacies determination. In this paper, a fuzzy inference methodology has been implemented as a MATLAB routine and applied for the first time to well log data from the German Continental Deep Drilling Program (KTB). The training of the fuzzy inference system is based on the analysis of the multi-class Matthews correlation coefficient computed for the classification matrix. For this particular data set, we have found that the best suited membership function type is the piecewise linear interpolation of the normalized histograms; that the best combination operator for obtaining the final lithology degrees of membership is the fuzzy gamma operator; and that all the available properties are relevant in the classification process. Results show that this fuzzy logic-based method is suited for rapidly and reasonably suggesting a lithology column from well log data, neatly identifying the main units and in some cases refining the classification, which can lead to a better interpretation. We have tested the trained system with synthetic data generated from property value distributions of the training data set to find that the differences in data distributions between both wells are significant enough to misdirect the inference process. However, a cross-validation analysis has revealed that, even with differences between data distributions and missing lithologies in the training data set, this fuzzy logic inference system is able to output a coherent classification.     

Collected Rain Water as Cost-Efficient Source for Aquifer Tracer Testing

Locally collected precipitation water can be actively used as a groundwater tracer solution based on four inherent tracer signals: electrical conductivity, stable isotopic signatures of deuterium [δ²H], oxygen-18 [δ¹⁸O], and heat, which all may strongly differ from the corresponding background values in the tested groundwater. In hydrogeological practice, a tracer test is one of the most important methods for determining subsurface connections or field parameters, such as porosity, dispersivity, diffusion coefficient, groundwater flow velocity, or flow direction. A common problem is the choice of tracer and the corresponding permission by the appropriate authorities. This problem intensifies where tracer tests are conducted in vulnerable conservation or water protection areas (e.g., around drinking water wells). The use of (if required treated) precipitation as an elemental groundwater tracer is a practical solution for this problem, as it does not introduce foreign matters into the aquifer system, which may contribute positively to the permission delivery. Before tracer application, the natural variations of the participating end members' tracer signals have to be evaluated locally. To obtain a sufficient volume of tracer solution, precipitation can be collected as rain using a detached, large-scale rain collector, which will be independent from possibly existing surfaces like roofs or drained areas. The collected precipitation is then stored prior to a tracer experiment.     

基于GPU的锥束CT体数据等值面重构和显示的改进

本文提出一种采用可编程显卡的计算能力来加速锥束CT体数据等值面重构的方法,并进行多方面的改进,有效地提高移动立方体(Marching Cubes)算法的性能。最后在表面绘制中引入深度剥离技术实现顺序无关透明度显示,对帮助理解锥束CT体数据和缺陷检测起到很好的效果