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https://github.com/jesperdramsch/seismic-transfer-learning

Deep-learning seismic facies on state-of-the-art CNN architectures
https://github.com/jesperdramsch/seismic-transfer-learning

deep-learning interpretation machine-learning malenov resnet-50 seismic transfer-learning vgg16

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

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README 

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

*[Jesper S. Dramsch](http://orcid.org/0000-0001-8273-905X), Technical University of Denmark, and Mikael Lüthje, Technical University of Denmark*



## Abstract

> We explore propagation of seismic interpretation by deep learning in stacked 2D sections. We show the application of state-of-the-art image classification algorithms on seismic data. These algorithms were trained on big labeled photograph databases. We use transfer learning to benefit from pre-trained networks and evaluate their performance on seismic data.



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Presentation Date: Wednesday, October 17, 2018

Start Time: 8:30:00 AM

Location: 204B (Anaheim Convention Center)

Presentation Type: Oral

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## Citation



### Paper

> Jesper S. Dramsch and Mikael Lüthje (2018) Deep-learning seismic facies on state-of-the-art CNN architectures. SEG Technical Program Expanded Abstracts 2018: pp. 2036-2040.



### Presentation

> Dramsch, Jesper Soeren; Lüthje, Mikael (2018): Deep-learning seismic facies on state-of-the-art CNN architectures. figshare. Presentation.

https://doi.org/10.6084/m9.figshare.7301645.v1



### Code

> Dramsch, Jesper Soeren; Lüthje, Mikael (2018): Deep-learning seismic facies on state-of-the-art CNN architectures. figshare. Code.

https://doi.org/10.6084/m9.figshare.7227545



## Usage



- Open the [Notebook](Seismic%20Classifiers-Pub.ipynb)

- Download the [F3 Seismic Data](https://terranubis.com/datainfo/Netherlands_Offshore_F3_Block_-_Complete)

- Download Models from the [Model Zoo](https://keras.io/applications/#models-for-image-classification-with-weights-trained-on-imagenet)

- Have Fun Experimenting



## Interpretation of VGG



![Interpretation of VGG](vgg1_i.png)



## Loss of VGG



![Loss of VGG](vgg1_loss.png)



## References



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## Notes

We explore transfer training for automatic seismic interpretation without fine-tuning.

See and cite the [Powerpoint](https://doi.org/10.6084/m9.figshare.7301645.v1)



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Read More: https://library.seg.org/doi/abs/10.1190/segam2018-2996783.1

Or at: https://dramsch.net/#portfolio