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Geostatistics for Environmental Applications Proceedings of the Fifth European Conference on Geostatistics for Environmental Applications /
Once applied only to problems of mining-reserves assessment or petroleum-reservoir characterization, geostatistics is now being used in an increasingly large number of disciplines in environmental sciences. On the one hand, it enables the analysis and handling, in a rigorous probabilistic framework...
| Clasificación: | Libro Electrónico |
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| Autor Corporativo: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2005.
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| Edición: | 1st ed. 2005. |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- Change of support: an inter-disciplinary challenge
- Combining categorical and continuous information using Bayesian Maximum Entropy
- Geostatistical prediction of spatial extremes and their extent
- Monitoring network optimisation using support vector machines
- Bayesian Kriging with lognormal data and uncertain variogram parameters
- Kriging of scale-invariant data: optimal parameterization of the autocovariance model
- Scaling Effects on Finite-Domain Fractional Brownian Motion
- The delineation of fishing times and locations for the Shark Bay scallop fishery
- A spatial extension of CART: application to classification of ecological data
- Using a Markov-type model to combine trawl and acoustic data in fish surveys
- Mapping unobserved factors on vine plant mortality
- Analysis and modelling of spatially and temporally varying phenological phases
- Detection of spatial clusters and outliers in cancer rates using geostatistical filters and spatial neutral models
- Geostatistical assessment of long term human exposure to air pollution
- Air quality models resulting from multi-source emissions
- Variogram estimation with noisy data in the space-time domain: application to air quality modelling
- Multiple-point geostatistics: a powerful tool to improve groundwater flow and transport predictions in multi-modal formations
- Simulation of radionuclide mass fluxes in a heterogeneous clay formation locally disturbed by excavation
- Modeling density-dependent flow using hydraulic conductivity distributions obtained by means of non-stationary indicator simulation
- Random field approach to seawater intrusion in heterogeneous coastal aquifers: unconditional simulations and statistical analysis
- Uncertainty estimation of well catchments: semianalytical post-processing
- Conditional moments of residence time of sorbent solutes under radial flow
- Impact of the choice of the variogram model on flow and travel time predictors in radial flows
- Strategies to determine dispersivities in heterogeneous aquifers
- Solving the groundwater inverse problem by successive flux estimation
- Inverse problem for highly heterogeneous porous media: the factorial geostatistical analysis in differential system method
- Inverse stochastic estimation of well capture zones with application to the Lauswiesen site (Tübingen, Germany)
- "Soft" geostatistical analysis of radioactive soil contamination
- Modelling the spatial distribution of copper in the soils around a metal smelter in northwestern Switzerland
- Towards a real-time multi-phase sampling strategy optimization
- Spatio-temporal mapping of sea floor sediment pollution in the North Sea
- Merging Landsat TM and SPOT-P images with geostatistical stochastic simulation
- Characterising local spatial variation in land cover using geostatistical functions and the discrete wavelet transform
- Distinguishing features from outliers in automatic Kriging-based filtering of MBES data: a comparative study
- Forecasting volcanic eruptions using geostatistical methods
- Delineation of estuarine management units: Evaluation of an automatic procedure
- Estimating indicators of river quality by geostatistics
- Stochastic simulation of rainfall using a spacetime geostatistical algorithm
- Inferring the lateral subsurface correlation structure from georadar data: Methodological background and experimental evidence.