Improving interpretation and modelling techniques in geoscience using deep generative networks (GANs)
Access Status
Open access
Date
2025Supervisor
Gretchen Benedix-Bland
Vladimir Puzyrev
Chris Elders
Ritu Gupta
Type
Thesis
Award
PhD
Metadata
Show full item recordFaculty
Science and Engineering
School
School of Earth and Planetary Sciences
Collection
Abstract
Generative Adversarial Networks are a novel, data-efficient method by which models are trained to learn from a limited amount of labelled data and to generate synthetic data, which reduces the burden of manual annotation. This thesis examines the generative adversarial power of GANs in geoscience, exploring how they can be used to unravel the underlying geological patterns and geostatistical features inherent in the geoscience data.
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