  We consider the application of machine learning to the evaluation of
geothermal resource potential. A supervised learning problem is defined where
maps of 10 geological and geophysical features within the state of Nevada, USA
are used to define geothermal potential across a broad region. We have
available a relatively small set of positive training sites (known resources or
active power plants) and negative training sites (known drill sites with
unsuitable geothermal conditions) and use these to constrain and optimize
artificial neural networks for this classification task. The main objective is
to predict the geothermal resource potential at unknown sites within a large
geographic area where the defining features are known. These predictions could
be used to target promising areas for further detailed investigations. We
describe the evolution of our work from defining a specific neural network
architecture to training and optimization trials. Upon analysis we expose the
inevitable problems of model variability and resulting prediction uncertainty.
Finally, to address these problems we apply the concept of Bayesian neural
networks, a heuristic approach to regularization in network training, and make
use of the practical interpretation of the formal uncertainty measures they
provide.
