  Electrical Resistivity Tomography (ERT) has been extensively used for imaging
the subsurface resistivity distribution and structure. Over the years, many
algorithms have been developed in order to solve the subsurface resistivity
distribution from the ERT measurements. In this paper a new method for
interpreting the ERT measurements is presented. Using supervised learning to
train a neural network, we are able to interpret the ERT measurement into a 2D
image of the underground resistivity up to depths of 50 meters while using a
simple Wenner-Schlumberger survey of 96 electrodes with 1 meter spacing. The
neural network is trained and tested using simulative data and it is shown to
have superior results over a well established inversion method.
