Mutually constrained inversion in combination with laterally constrained inversion (MCI-LCI) between transient electromagnetic (TEM) and direct current (DC) resistivity methods was successfully used to characterize a buried valley structure. Although both methods measure, in some sense, the electrical resistivity, or conductivity, of the subsurface, they sample different volumes and have different sensitivities, which are exploited with mutually and laterally constrained inversion of combined, coincident profile data sets. The output models incorporate the information from both data sets to obtain optimum layered 1D models, fitting both data sets and constraints. The set-up of constraints contains three parts. First, we constrain the individual data sets along their profile using lateral constraints producing a chain of TEM data and a chain of DC data. Next, we merge the information from these two chains by setting up mutual constraints between the TEM and the DC models. Finally, we adjust the mutual constraints to resemble the increasing sampling volumes with depth, i.e. wide constraints at large depths and short constraints at shallow depths. All data sets are inverted simultaneously; a common objective function is minimized, and the number of output models is equal to the number of 1D soundings. The lateral and mutual constraints are part of the inversion, and consequently the output models are balanced between the constraints and the data-model fit. Information from one model will spread to the neighbouring models through the constraints, helping to resolve parameters that are poorly resolved by any of the individual data sets. A field example illustrates that MCI-LCI allows the governing information from each method to dominate the inversion process. Thus, the model resolution in both the shallow and the deeper parts of the model is significantly enhanced. This could not be obtained by inverting the two data sets separately with a subsequent comparison of the output models. Our results are confirmed by drill-hole data.
- Programområde 2: Vandressourcer