kasyful / seismic-transfer-learning

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

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

Jesper S. Dramsch, 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.

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
• Download the F3 Seismic Data
• Download Models from the Model Zoo
• Have Fun Experimenting

Interpretation of VGG

Loss of VGG

References

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Notes

We explore transfer training for automatic seismic interpretation without fine-tuning. See and cite the Powerpoint

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Read More: https://library.seg.org/doi/abs/10.1190/segam2018-2996783.1 Or at: https://dramsch.net/#portfolio