西安地电台环境噪声干扰的实验研究

对西安地电台周围环境噪声声干扰进行了实验研究，结果表明，环境噪声的衰减是很快的，地电测区避开企带来单位２００ｍ，环境噪声电平即可衰减１个数量级。只工作认真仔细，在大城市近郊能够选出较好的地电台址。     

Bottom friction caused by boundary layer streaming beneath random waves for laminar and smooth turbulent flow

The effect of boundary layer streaming on the sea bed shear stresses, beneath random waves, is investigated for laminar flow as well as smooth turbulent flow. It is demonstrated how bottom friction formulas for regular waves can be used to obtain the bed shear stresses resulting from steady streaming under random waves. As a result, friction factors for steady streaming under random waves are provided, and the effect of streaming versus the effect of linear waves is discussed. For laminar flow the effect of second order Stokes waves is also included. Examples are included to illustrate the applicability of the present practical method, and results are obtained using data typical for field conditions.     

Model parameter optimization for site-dependent simulation of ground motion by simulated annealing: Re-evaluation of the Ashigara Valley prediction experiment

More than 40 groups from 10 different countries participated in a weak- and strong-motion prediction experiment at Ashigara Valley which required the blind prediction of time series, spectra and spectral ratios for selected and instrumented sedimentary sites with well-known geotechnical properties. The wide scatter of the results of this experiment have raised a number of questions as to how to model high-frequency ground motion in the presence of available geotechnical and geophysical data. Using a simulated annealing waveform inversion method, we have tried to optimize and automate the model construction for ID site-dependent ground-motion simulation. We found a whole set of successfull models which provide good waveform fit (r > 0.8) for the observed displacement records at site KS2 but also yield sufficiently accurate response spectra and peak value predictions for both surface and downhole site. This shows that ID models are fully adequate to model the site conditions at least for the weak motion data. The resulting successful layer models consistently show a slower, less dense, and slightly thicker low velocity coverage with stronger damping than the official geotechnical model. Furthermore, their statistical properties directly measure the sensitivity of the individual parameters for the simulations. The critical re-evaluation of our own prediction which was based on stochastic simulation shows that although this approach has its greatest merits in situations where little information is available, it can also be successfully applied to model individual records if sufficient care is taken to determine the source parameters. Simulated annealing waveform inversion has shown to be a powerful tool to optimize that process.     

Well Conditioning in Object Models

This paper presents two object models with corresponding simulation algorithms, which aim to condition well data correctly while still converging in reasonable time. The first model is devoted to fluvial channels and the second one is mainly intended for smaller objects. To verify the conditioning, a method for validating well conditioning algorithms for object models is given. The purpose is to determine the extent to which the well conditioning introduces a bias in the models. To do this, we check that the double expectation of a parameter conditioned to wells is equal to the unconditional expectation. This method is applied to two different object models. Both the conditioning algorithms presented here give good results using this test.     

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.     

The North Atlantic Oscillation as a source of stochastic forcing of the wind-driven ocean circulation

Observations show that at middle and high latitudes, the magnitude of stochastic wind stress forcing due to atmospheric weather is comparable to that of the seasonal cycle and will likely exert a significant influence on the ocean circulation. The focus of this work will be the contribution of the North Atlantic Oscillation (NAO) to the stochastic forcing in the North Atlantic and its influence on the large-scale, wind-driven ocean circulation. To this end, a QG model of the North Atlantic Ocean was forced with the stochastic component of wind stress curl associated with the NAO signal. The ocean response is localized primarily in the western boundary region and can be conveniently understood using generalized stability analysis. Much of the variability is associated with the nonnormal influence of the bathymetry and inhomogeneities in the western boundary flow on the large-scale circulation. A more traditional statistical analysis of the circulation, however, reveals that there are very small and insignificant correlations between the NAO forcing and the ocean response within the western boundary region. This suggests that the dynamics of the ocean response to stochastic forcing may obscure any obvious coherence between the forcing and the response which is equally difficult to identify from observations.     

Assessment of uncertainty associated with the estimation of well catchments by moment equations

Non-local stochastic moment equations are used successfully to analyze groundwater flow in randomly heterogeneous media. Here we present a moment equations-based approach to quantify the uncertainty associated with the estimation of well catchments. Our approach is based on the development of a complete second order formalism which allows obtaining the first statistical moments of the trajectories of conservative solute particles advected in a generally non-uniform groundwater flow. Approximate equations of moments of particles’ trajectories are then derived on the basis of a second order expansion in terms of the standard deviation of the aquifer log hydraulic conductivity. Analytical expressions are then obtained for the predictors of locations of mean stagnation points, together with their associated uncertainties. We implement our approach on heterogeneous media in bounded two-dimensional domains, with and without including the effect of conditioning on hydraulic conductivity information. The impact of domain size, boundary conditions, heterogeneity and non-stationarity of hydraulic conductivity on the prediction of a well catchment is explored. The results are compared against Monte Carlo simulations and semi-analytical solutions available in the literature. The methodology is applicable to both infinite and bounded domains and is free of distributional assumptions (and so applies to both Gaussian and non-Gaussian log hydraulic conductivity fields) and formally includes the effect of conditioning on available information.     

Assimilation of snow covered area information into hydrologic and land-surface models

This paper describes a data assimilation method that uses observations of snow covered area (SCA) to update hydrologic model states in a mountainous catchment in Colorado. The assimilation method uses SCA information as part of an ensemble Kalman filter to alter the sub-basin distribution of snow as well as the basin water balance. This method permits an optimal combination of model simulations and observations, as well as propagation of information across model states. Sensitivity experiments are conducted with a fairly simple snowpack/water-balance model to evaluate effects of the data assimilation scheme on simulations of streamflow. The assimilation of SCA information results in minor improvements in the accuracy of streamflow simulations near the end of the snowmelt season. The small effect from SCA assimilation is initially surprising. It can be explained both because a substantial portion of snowmelts before any bare ground is exposed, and because the transition from 100% to 0% snow coverage occurs fairly quickly. Both of these factors are basin-dependent. Satellite SCA information is expected to be most useful in basins where snow cover is ephemeral. The data assimilation strategy presented in this study improved the accuracy of the streamflow simulation, indicating that SCA is a useful source of independent information that can be used as part of an integrated data assimilation strategy.     

Designing a monitoring network for detecting groundwater pollution with stochastic simulation and a cost model

A method is presented to design monitoring networks for detecting groundwater pollution at industrial sites. The goal is to detect the pollution at some distance from the site’s boundary so that it can be cleaned up or hydrologically contained before contaminating groundwater outside the site. It is assumed that pollution may occur anywhere on the site, that transport is by advection only and that no retardation and chemical reactions take place. However, the approach can be easily extended to include designated (and uncertain) source areas, dispersion and reactive transport. The method starts from the premise that it is impossible to detect 100% of all the contaminant plumes with reasonable costs and therefore seeks a balance between the risk of pollution and network density. The design approach takes account of uncertainty in the flow field by simulating realisations of conductivity, groundwater head and associated flow fields, using geostatistical simulation and a groundwater flow model. The realisations are conditioned to conductivity and head observations that may already be present on the site. The result is an ensemble of flow fields that is further analysed using a particle track program. From this the probability of missing a contaminant plume originating anywhere on the terrain can be estimated for a given network. From this probability follows the risk, i.e. the expected costs of an undetected pollution. The total costs of the monitoring strategy are calculated by adding the risk of pollution to the costs of installing and maintaining the monitoring wells and the routinely performed chemical analyses. By repeating this procedure for networks of varying well numbers, the best network is chosen as the one that minimises total cost. The method is illustrated with a simulated example showing the added worth of exploratory wells for characterising hydraulic conductivity of a site.     

Stochastic modeling of solute transport in 3-D heterogeneous porous media with random source condition

During probabilistic analysis of flow and transport in porous media, the uncertainty due to spatial heterogeneity of governing parameters are often taken into account. The randomness in the source conditions also play a major role on the stochastic behavior in distribution of the dependent variable. The present paper is focused on studying the effect of both uncertainty in the governing system parameters as well as the input source conditions. Under such circumstances, a method is proposed which combines with stochastic finite element method (SFEM) and is illustrated for probabilistic analysis of concentration distribution in a 3-D heterogeneous porous media under the influence of random source condition. In the first step SFEM used for probabilistic solution due to spatial heterogeneity of governing parameters for a unit source pulse. Further, the results from the unit source pulse case have been used for the analysis of multiple pulse case using the numerical convolution when the source condition is a random process. The source condition is modeled as a discrete release of random amount of masses at fixed intervals of time. The mean and standard deviation of concentration is compared for the deterministic and the stochastic system scenarios as well as for different values of system parameters. The effect of uncertainty of source condition is also demonstrated in terms of mean and standard deviation of concentration at various locations in the domain.