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.

翻译：我们探讨了机器学习在地热资源潜力评估中的应用。定义了一个监督学习问题，利用美国内华达州10种地质与地球物理特征图，在广阔区域内划定地热潜力。我们拥有少量正样本训练点（已知资源或运行中的发电站）和负样本训练点（钻探证实地热条件不适宜的井位），并以此约束和优化用于分类任务的人工神经网络。主要目标是预测已知特征分布的大地理区域内未知站点的地热资源潜力，这些预测可用于锁定值得进一步详查的靶区。本文阐述了从定制特定神经网络架构到训练与优化试验的工作演进过程。经分析，我们揭示了模型变异及由此产生的预测不确定性这一必然问题。最终，为应对这些挑战，我们引入贝叶斯神经网络概念——一种启发式网络训练正则化方法，并利用其提供的正式不确定性度量进行实用化解析。