JesperDramsch/seismic-transfer-learning

Deep-learning seismic facies on state-of-the-art CNN architectures

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This project helps geophysicists and exploration seismologists automatically identify different geological features, known as seismic facies, in 2D seismic survey images. By applying advanced image classification models pre-trained on vast photograph databases, it takes raw seismic sections and produces classified interpretations of subsurface structures. This allows specialists to quickly analyze and understand geological formations without extensive manual effort.

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Use this if you need to quickly and automatically classify seismic facies in 2D seismic data and want to leverage the power of pre-trained deep learning models without extensive fine-tuning.

Not ideal if you require a robust solution for 3D seismic interpretation or need models specifically trained from scratch on proprietary seismic datasets with extensive fine-tuning capabilities.

seismic-interpretation geophysics oil-gas-exploration subsurface-modeling geological-mapping
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 9 / 25
Maturity 16 / 25
Community 22 / 25

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Stars

89

Forks

55

Language

Jupyter Notebook

License

MIT

Last pushed

May 14, 2022

Commits (30d)

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