The non-inductive galvanic disturbances due to surficial bodies, lying smaller than high frequency skin depth, cause serious interpretational errors in magnetotelluric data. These frequency independent distortions result in a quasi-static shift between the apparent resistivity curves known as static shift. Two-dimensional modelling studies, for the effects of surficial bodies on magnetotelluric interpretation, show that the transverse electric (TE) mode apparent resistivity curves are hardly affected compared to the transverse magnetic (TM) mode curves, facilitating the correction by using a curve shifting method to match low frequency asymptotes. But in the case of field data the problem is rather complicated because of the random distribution of geometry and conductivity of near surface inhomogeneities. Here we present the use of deep resistivity sounding (DRS) data to constrain MT static shift. Direct current sensitivity studies show that the behaviour of MT static shift can be estimated using DC resistivity measurements close to the MT sounding station to appreciable depths. The distorted data set is corrected using the MT response for DRS model and further subject to joint inversion with DRS data. Joint inversion leads to better estimation of MT parameters compared to the separate inversion of data sets.

Initialization of model parameters is crucial in the conventional 1D inversion of DC electrical data, since a poor guess may result in undesired parameter estimations. In the present work, we investigate the performance of neural networks in the direct inversion of DC sounding data, without the need ofa priori information. We introduce a two-step network approach where the first network identifies the curve type, followed by the model parameter estimation using the second network. This approach provides the flexibility to accommodate all the characteristic sounding curve types with a wide range of resistivity and thickness. Here we realize a three layer feed-forward neural network with fast back propagation learning algorithms performing well. The basic data sets for training and testing were simulated on the basis of available deep resistivity sounding (DRS) data from the crystalline terrains of south India. The optimum network parameters and performance were decided as a function of the testing error convergence with respect to the network training error. On adequate training, the final weights simulate faithfully to recover resistivity and thickness on new data. The small discrepancies noticed, however, are well within the resolvability of resistivity sounding curve interpretations.