Enrich the interpretation of seismic image segmentation by estimating epistemic uncertainty
Tao Zhao, Xiaoli Chen
Abstract
In this study, we discuss the uncertainty involved in machine learning-based seismic image segmentation. Using a salt body detection example, we demonstrate that while often referred to as probability, the output from a single machine learning prediction run provides neither an adequate estimate of the true probability nor uncertainty. We use Monte Carlo dropout as the method to estimate epistemic uncertainty, while also providing a more appropriate estimate of the prediction probability. These results help us better understand the machine learning output. Presentation Date: Monday, October 12, 2020 Session Start Time: 1:50 PM Presentation Time: 2:40 PM Location: 351F Presentation Type: Oral
Topics & Concepts
Artificial intelligenceMachine learningComputer scienceUncertainty quantificationInterpretation (philosophy)Convolutional neural networkArtificial neural networkBayesian inferenceBayesian probabilityAlgorithmProgramming languageSeismic Imaging and Inversion TechniquesReservoir Engineering and Simulation MethodsHydraulic Fracturing and Reservoir Analysis