Mutual information analysis to approach nonlinearity in groundwater stochastic fields
| Autor: | Ilaria Butera, Luca Vallivero, Luca Ridolfi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Environmental Engineering
010504 meteorology & atmospheric sciences Computer science 0208 environmental biotechnology Monte Carlo method 02 engineering and technology Linkage (mechanical) Information theory 01 natural sciences law.invention Groundwater stochastic fields law Environmental Chemistry Statistical physics Safety Risk Reliability and Quality Nonlinearity Mutual information Heterogeneous transmissivity fields 0105 earth and related environmental sciences General Environmental Science Water Science and Technology Field (geography) 020801 environmental engineering Nonlinear system Flow (mathematics) Random variable |
| Popis: | In heterogeneous porous media, transmissivity can be regarded as a spatial stochastic variable. Transmissivity fluctuations induce stochasticity in the groundwater velocity field and transport features. In order to model subsurface phenomena, it is important to understand the relationships that exist between the variables that characterize flow and transport. Linear relationships are easier to deal with. Nevertheless, it is well known that flow and transport variables exhibit interdependences that become more and more nonlinear as the heterogeneity increases. The aim of this work is to draw attention to the information contained in nonlinear linkages, and to show that it can be of great relevance with respect to the linear information content. Information theory tools are proposed to detect the presence of nonlinear components. By comparing the cross-covariance function and mutual information, the amount of linear linkage is compared with nonlinear linkage. In order to avoid analytical approximations, data from Monte Carlo simulations of heterogeneous transmissivity fields have been considered in the analysis. The obtained results show that the presence of nonlinear components can be relevant, even when the cross-covariance values are nil. |
| Databáze: | OpenAIRE |
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