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We consider the application of machine learning to the evaluation ofgeothermal resource potential. A supervised learning problem is defined wheremaps of 10 geological and geophysical features within the state of Nevada, USAare used to define geothermal potential across a broad region. We haveavailable a relatively small set of positive training sites (known resources oractive power plants) and negative training sites (known drill sites withunsuitable geothermal conditions) and use these to constrain and optimizeartificial neural networks for this classification task. The main objective isto predict the geothermal resource potential at unknown sites within a largegeographic area where the defining features are known. These predictions couldbe used to target promising areas for further detailed investigations. Wedescribe the evolution of our work from defining a specific neural networkarchitecture to training and optimization trials. Upon analysis we expose theinevitable problems of model variability and resulting prediction uncertainty.Finally, to address these problems we apply the concept of Bayesian neuralnetworks, a heuristic approach to regularization in network training, and makeuse of the practical interpretation of the formal uncertainty measures theyprovide.

Bayesian Neural Networks for Geothermal Resource Assessment: Prediction with Uncertainty