Employing MS-UNets Networks for Multiscale 3-D Gravity Data Inversion: A Case Study in the Nordkapp Basin, Barents Sea

Salt domes are very important in hydrocarbon exploration and identification of potential drilling hazards. While seismic data are indispensable for detailed subsurface imaging, especially in delineating the geometry and properties of salt bodies and their boundaries, gravity inversion provides an ad...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-13
Hauptverfasser:	Wang, Rui, Ding, Yaming, Xu, Zhengwei, Zhdanov, Michael S., Xian, Minghao, Zhang, Yu, Li, Jun, Jiang, Chao, Guo, Ziqing
Format:	Artikel
Sprache:	eng
Schlagworte:	3-D inversion Algorithms Artificial neural networks Data interpretation Data models Domes Drilling Geological structures Geology Geometry Geophysical data Gravity gravity and gravity gradiometry Gravity data Hazard identification Inverse problems Machine learning multiscale UNets (MS-UNets) Neural networks Salt domes Salts Seismic data Solid modeling Three-dimensional displays Training
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!