Bayesian Neural Networks for Geothermal Resource Assessment: Prediction with Uncertainty
This work addresses geothermal resource assessment for energy exploration, but it is incremental as it applies an existing Bayesian method to a new domain-specific dataset.
The paper tackles the problem of predicting geothermal resource potential in Nevada using geological and geophysical features with a small dataset, applying Bayesian neural networks to address model variability and provide uncertainty estimates.
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.