Bayesian Neural Networks for Geothermal Resource Assessment: Prediction with Uncertainty

• URL: http://arxiv.org/abs/2209.15543v3
• Date: Thu, 26 Oct 2023 02:04:08 GMT
• Title: Bayesian Neural Networks for Geothermal Resource Assessment: Prediction with Uncertainty
• Authors: Stephen Brown, William L. Rodi, Marco Seracini, Chen Gu, Michael Fehler, James Faulds, Connor M. Smith, and Sven Treitel
• Abstract summary: 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.
• Score: 0.8331498366387238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Enhancing Worldwide Image Geolocation by Ensembling Satellite-Based Ground-Level Attribute Predictors [4.415977307120618]
  We examine the challenge of estimating the location of a single ground-level image in the absence of GPS or other location metadata. We introduce a novel metric, Recall vs Area, which measures the accuracy of estimated distributions of locations. We then examine an ensembling approach to global-scale image geolocation, which incorporates information from multiple sources.
  arXiv  Detail & Related papers  (2024-07-18T19:15:52Z)
• Towards physics-informed neural networks for landslide prediction [1.03590082373586]
  PINN is a neural network tasked with explicitly retrieving geotechnical parameters from common proxy variables. Our model produces excellent predictive performance in the form of standard susceptibility output. This architecture is framed to tackle coseismic landslide prediction, something that, if confirmed in other studies, could open up towards PINN-based near-real-time predictions.
  arXiv  Detail & Related papers  (2024-07-09T11:54:49Z)
• Enhancing Lithological Mapping with Spatially Constrained Bayesian Network (SCB-Net): An Approach for Field Data-Constrained Predictions with Uncertainty Evaluation [0.0]
  The SCB-Net aims to effectively exploit the information from auxiliary variables while producing spatially constrained predictions. The SCB-Net has been applied to two selected areas in northern Quebec, Canada. This study highlights the promising advancements of deep neural networks in geolinear, particularly in handling complex spatial feature learning tasks.
  arXiv  Detail & Related papers  (2024-03-29T14:17:30Z)
• The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning [73.5095051707364]
  We consider classical distribution-agnostic framework and algorithms minimising empirical risks. We show that there is a large family of tasks for which computing and verifying ideal stable and accurate neural networks is extremely challenging.
  arXiv  Detail & Related papers  (2023-09-13T16:33:27Z)
• Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction [50.79827516897913]
  We introduce an interpretable paradigm for trajectory prediction that distributes the uncertainty among semantic concepts. We validate our approach on real-world autonomous driving data, demonstrating superior performance over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-11-16T06:28:20Z)
• Neural network interpretability for forecasting of aggregated renewable generation [0.0]
  There is a need for aggregated prosumers to predict solar power generation. This paper presents two interpretable neural networks to solve the problem.
  arXiv  Detail & Related papers  (2021-06-19T11:50:26Z)
• A neural anisotropic view of underspecification in deep learning [60.119023683371736]
  We show that the way neural networks handle the underspecification of problems is highly dependent on the data representation. Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
  arXiv  Detail & Related papers  (2021-04-29T14:31:09Z)
• Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty [91.0564497403256]
  We present a novel framework that involves probabilistic fusion between the two families of predictions during network training. Our network features a self-attention graph neural network, which drives the learning by enforcing strong interactions between different correspondences. We propose motion parmeterizations suitable for learning and show that our method achieves state-of-the-art performance on the challenging DeMoN and ScanNet datasets.
  arXiv  Detail & Related papers  (2021-04-16T17:59:06Z)
• A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning [95.27249880156256]
  We develop and present a unified architecture of network community-finding methods. We introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
  arXiv  Detail & Related papers  (2021-01-03T02:32:45Z)
• Uncertainty Estimation and Sample Selection for Crowd Counting [87.29137075538213]
  We present a method for image-based crowd counting that can predict a crowd density map together with the uncertainty values pertaining to the predicted density map. A key advantage of our method over existing crowd counting methods is its ability to quantify the uncertainty of its predictions. We show that our sample selection strategy drastically reduces the amount of labeled data needed to adapt a counting network trained on a source domain to the target domain.
  arXiv  Detail & Related papers  (2020-09-30T03:40:07Z)
• Bayesian deep learning for mapping via auxiliary information: a new era for geostatistics? [3.5450828190071655]
  We show how deep neural networks can learn the complex relationships between point-sampled target variables and gridded auxiliary variables. We obtain uncertainty estimates via a Bayesian approximation known as Monte Carlo dropout.
  arXiv  Detail & Related papers  (2020-08-17T13:56:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.