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

DOI

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.

Identifier
DOI https://doi.org/10.5880/GFZ.2.5.2021.001
Related Identifier https://doi.org/10.31223/X5S88B
Metadata Access http://doidb.wdc-terra.org/oaip/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:doidb.wdc-terra.org:7126
Provenance
Creator Wrona, Thilo ORCID logo; Pan, Indranil ORCID logo; Bell, Rebecca ORCID logo; Gawthorpe, Robert ORCID logo; Fossen, Haakon ORCID logo; Brune, Sascha ORCID logo
Publisher GFZ Data Services
Contributor Wrona, Thilo
Publication Year 2021
Funding Reference The Norwegian Academy of Science and Letters, 6269; Helmholtz-Gemeinschaft; Geo.X Network
Rights CC0 Universal 1.0; http://creativecommons.org/publicdomain/zero/1.0/
OpenAccess true
Contact Wrona, Thilo (GFZ German Research Centre for Geosciences, Potsdam, Germany)
Representation
Resource Type Software
Version 0.1
Discipline Geosciences