3-D seismic interpretation with deep learning: a set of Python tutorials

Description: Here we are sharing our code, tutorials and examples used to interpret geological structures (e.g. faults, salt bodies and horizones) in 2-D and/or 3-D seismic reflection data using deep learning. The repository is organised in a series of tutorials (Jupyter notebooks) with increasing degree of difficulty. We show step-by-step how to: (1) load seismic data, (2) train a model and (3) apply the model to map different geological structures. You can find a few visual examples on our poster and more technical details in our preprint.

Global identifier:

Doi(
    "10.5880/GFZ.2.5.2021.001",
)

Tags: Lagerstätte ? Geoelektrik ? Künstliche Intelligenz ? Daten ? Geowissenschaften ? EARTH SCIENCE > OCEANS > MARINE GEOPHYSICS ? EARTH SCIENCE > SOLID EARTH > TECTONICS ? Seismic interpretation ? Seismic reflection data ?

License: Creative Commons Zero

Language: Englisch/English

Organisations

• GFZ Data Services (Herausgeberschaft)
• GFZ Helmholtz-Zentrum für Geoforschung (Bereitstellung)

Persons

• Bell, Rebecca (Verfasser*in)
• Brune, Sascha (Verfasser*in)
• Fossen, Haakon (Verfasser*in)
• Gawthorpe, Robert (Verfasser*in)
• Pan, Indranil (Verfasser*in)
• Wrona, Thilo (Verfasser*in)
• Wrona, Thilo (Beitragende*r)

Issued: 2021-01-01

Status

Aktiv

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