  Deep neural networks (DNNs) offer a real-time solution for the inversion of
borehole resistivity measurements to approximate forward and inverse operators.
It is possible to use extremely large DNNs to approximate the operators, but it
demands a considerable training time. Moreover, evaluating the network after
training also requires a significant amount of memory and processing power. In
addition, we may overfit the model. In this work, we propose a scoring function
that accounts for the accuracy and size of the DNNs compared to a reference DNN
that provides a good approximation for the operators. Using this scoring
function, we use DNN architecture search algorithms to obtain a quasi-optimal
DNN smaller than the reference network; hence, it requires less computational
effort during training and evaluation. The quasi-optimal DNN delivers
comparable accuracy to the original large DNN.
