Machine learning-enabled velocity model building with uncertainty quantification

Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration projects. Traditional velocity model building methods such as Full-Waveform Inversion (FWI) are powerful but often struggle wit...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	arXiv.org 2024-11
Hauptverfasser:	Orozco, Rafael, Huseyin Tuna Erdinc, Zeng, Yunlin, Louboutin, Mathias, Herrmann, Felix J
Format:	Artikel
Sprache:	eng
Schlagworte:	Constraint modelling Datasets Geophysical methods Image quality Inverse problems Machine learning Propagation velocity Statistical analysis Synthetic data Uncertainty Velocity Waveforms Workflow
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!