WISE: Full-waveform variational inference via subsurface extensions
Ziyi Yin, Rafael Orozco, Mathias Louboutin, Felix J. Herrmann
Abstract
ABSTRACT We introduce a probabilistic technique for full-waveform inversion, using variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.
Topics & Concepts
InferenceWaveformAmplitudeBayesian inferenceComputer scienceProbabilistic logicGenerative grammarInversion (geology)AlgorithmBayesian probabilityArtificial intelligenceGeologyPhysicsSeismologyQuantum mechanicsTelecommunicationsTectonicsRadarSeismic Imaging and Inversion TechniquesSeismic Waves and AnalysisReservoir Engineering and Simulation Methods