Automatic transport model data assimilation in Laplace space

Abstract

Emerging in situ sensors and distributed network technologies have the potential to monitor dynamic hydrological and environmental processes more effectively than traditional monitoring and data acquisition techniques by sampling at greater spatial and temporal resolutions. Since sensor networks supply data with little or no delay, applications exist where automatic or real-time assimilation of this data would be useful, for example, during smart remediation procedures where tracking of the plume response will reinforce real-time decisions. As a foray into this new data context, we consider the estimation of hydraulic conductivity when incorporating subsurface plume concentration data. Current practice optimizes the model in the time domain, which is often slow and very nonlinear. Instead, we perform model inversion in Laplace space and are able to do so because data gathered using new technologies can be sampled densely in time. An intermediate-scale synthetic aquifer is used to illustrate the developed technique. © 2012 by the American Geophysical Union.