Speaker
Description
The highly compressible nature of some aquitards leads to nonlinear consolidation where the groundwater flow parameters are stress-dependent. The case is further complicated by the heterogeneity of the hydrogeologic and geotechnical properties of the aquitards. To adequately model land subsidence in these systems, we develop a modeling approach to couple a nonlinear 1-D groundwater flow and consolidation model with a data assimilation scheme based on ensemble Kalman filter. This modeling approach allows to estimate the ensemble mean distribution of state variables and stress-dependent parameters, such as hydraulic conductivity (K), pore-pressure and total settlement. Zapata-Norberto et al. (2017) have shown that in randomly heterogeneous highly compressible aquitards under 1-D vertical flow, the parameter with largest impact on ensemble total settlement and its variance is K. We therefore consider the case where only K is randomly heterogeneous. We consider cases where pore-pressure and/or K measurements are available at given time intervals. We test our approach by solving the nonlinear flow and consolidation problem on a generated 1-D realization of lnK with exponential spatial correlation. These results are taken as our “true” solution. We take pore-pressure and/or lnK “measurements” at different times from this “true” solution. The ensemble Kalman filter method is then employed to estimate ensemble mean distribution of lnK, pore-pressure and total settlement based on the sequential assimilation of those measurements. The ensemble-mean estimates from this procedure closely approximate those from the “true” solution. This procedure can be easily extended to other random variables such as compression index and void ratio.
References
Zapata-Norberto B., Morales-Casique E. & Herrera G.S. Hydrogeology Journal (2017). https://doi.org/10.1007/s10040-017-1698-6
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