| Abstract: | 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. |
