基于构造约束的波阻抗边界刻画

通过边界保护正则化和约束反演,在反演的目标函数中引入各种先验信息约束,以解决波阻抗反演的病态问题和带限问题.为了克服波阻抗模型边界过于平滑,在反演中引入地层和断层等构造信息约束,并且通过调整地层分界面和断层处的正则参数值来实现构造约束.此外,采用各向异性扩散法进行平滑处理,改善反演结果.通过合成数据测试和实际资料反演,证明了本文提出的方法对刻画模型边界是有效的.     

Assessing uncertainties in a conceptual water balance model using Bayesian methodology / Estimation bayésienne des incertitudes au sein d’une modélisation conceptuelle de bilan hydrologique

Abstract

Abstract The aim of this study was to estimate the uncertainties in the streamflow simulated by a rainfall–runoff model. Two sources of uncertainties in hydrological modelling were considered: the uncertainties in model parameters and those in model structure. The uncertainties were calculated by Bayesian statistics, and the Metropolis-Hastings algorithm was used to simulate the posterior parameter distribution. The parameter uncertainty calculated by the Metropolis-Hastings algorithm was compared to maximum likelihood estimates which assume that both the parameters and model residuals are normally distributed. The study was performed using the model WASMOD on 25 basins in central Sweden. Confidence intervals in the simulated discharge due to the parameter uncertainty and the total uncertainty were calculated. The results indicate that (a) the Metropolis-Hastings algorithm and the maximum likelihood method give almost identical estimates concerning the parameter uncertainty, and (b) the uncertainties in the simulated streamflow due to the parameter uncertainty are less important than uncertainties originating from other sources for this simple model with fewer parameters.     

磁性下界面的正则化反演方法

根据磁异常反演磁性介质下界面,属于非线性问题,一般很难直接求解.本文采用将非线性问题线性化措施,建立起线性离散磁模型,然后应用线性规划的正则算法,设计了迭代求解方案.该方法不要求已知界面起伏的平均深度,只要求假定界面是连续的及磁性沿横向变化.算法便于对解施行物理约束,具有收敛快,精度高的特点.     

Evaluation of risk concepts in partial duration series

Conventional design practice aims at obtaining optimal estimates of floods with specified exceedance probabilities. Such estimates are, however, known on the average to be exceeded more frequently than expected. Alternatively, methods focusing on the expected exceedance probability can be used. Two different methods are considered here; the first is based on the sample distribution of true exceedance probabilities. The second is a Bayesian analogue using the likelihood function and a noninformative prior to describe the variability of exceedance probabilities. Appropriate analytical solutions are presented in both cases using the partial duration series approach.     

Rainfall estimation using raingages and radar — A Bayesian approach: 1. Derivation of estimators

Procedures for estimating rainfall from radar and raingage observations are constructed in a Bayesian framework. Given that the number of raingage measurements is typically very small, mean and variance of gage rainfall are treated as uncertain parameters. Under the assumption that log gage rainfall and log radar rainfall are jointly multivariate normal, the estimation problem is equivalent to lognormal co-kriging with uncertain mean and variance of the gage rainfall field.The posterior distribution is obtained under the assumption that the prior for the mean and inverse of the variance of log gage rainfall is normal-gamma 2. Estimate and estimation variance do not have closed-form expressions, but can be easily evaluated by numerically integrating two single integrals. To reduce computational burden associated with evaluating sufficient statistics for the likelihood function, an approximate form of parameter updating is given. Also, as a further approximation, the parameters are updated using raingage measurements only, yielding closed-form expressions for estimate and estimation variance in the Gaussian domain.With a reduction in the number of radar rainfall data in constructing covariance matrices, computational requirements for the estimation procedures are not significantly greater than those for simple co-kriging. Given their generality, the estimation procedures constructed in this work are considered to be applicable in various estimation problems involving an undersampled main variable and a densely sampled auxiliary variable.     

Simultaneous iterative time-domain sparse deconvolution to teleseismic receiver functions

In this paper, we present a new approach to estimate high-resolution teleseismic receiver functions using a simultaneous iterative time-domain sparse deconvolution. This technique improves the deconvolution by using reweighting strategies based on a Cauchy criterion. The resulting sparse receiver functions enhance the primary converted phases and its multiples. To test its functionality and reliability, we applied this approach to synthetic experiments and to seismic data recorded at station ABU, in Japan. Our results show Ps conversions at approximately 4.0 s after the primary P onset, which are consistent with other seismological studies in this area. We demonstrate that the sparse deconvolution is a simple, efficient technique in computing receiver functions with significantly greater resolution than conventional approaches.     

A model for estimating the value of sampling programs and the optimal number of samples for contaminated soil

A model is presented for estimating the value of information of sampling programs for contaminated soil. The purpose is to calculate the optimal number of samples when the objective is to estimate the mean concentration. A Bayesian risk–cost–benefit decision analysis framework is applied and the approach is design-based. The model explicitly includes sample uncertainty at a complexity level that can be applied to practical contaminated land problems with limited amount of data. Prior information about the contamination level is modelled by probability density functions. The value of information is expressed in monetary terms. The most cost-effective sampling program is the one with the highest expected net value. The model was applied to a contaminated scrap yard in Göteborg, Sweden, contaminated by metals. The optimal number of samples was determined to be in the range of 16–18 for a remediation unit of 100 m². Sensitivity analysis indicates that the perspective of the decision-maker is important, and that the cost of failure and the future land use are the most important factors to consider. The model can also be applied for other sampling problems, for example, sampling and testing of wastes to meet landfill waste acceptance procedures.