Uncertainty assessment in unsupervised machine-learning methods for deepwater channel seismic facies using outcrop-derived 3D models and synthetic seismic data
Karelia La Marca,
Heather Bedle,
Lisa Stright
et al.
Abstract:Unsupervised machine learning (ML) techniques have been widely applied to analyze seismic reflection data, including the identification of seismic facies and structural features. However, interpreting the resulting clusters often relies on geoscientists’ expertise, necessitating a robustness assessment of these methods. To evaluate their reliability, synthetic data generated from an actual outcrop model were employed to demonstrate how two unsupervised methods, Self-Organizing Maps (SOM) and Generative Topogra… Show more
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