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3: Gaussian Mixture Models for Robust Unsupervised Scanning-Electron Microscopy Image Segmentation of North Sea Chalk

Dramsch, J. S., Amour, F., & Lüthje, M. (2018, November). Gaussian Mixture Models for Robust Unsupervised Scanning-Electron Microscopy Image Segmentation of North Sea Chalk. In First EAGE/PESGB Workshop Machine Learning.

  • Github: https://github.com/JesperDramsch/backscatter-sem-segmentation

4: Deep learning seismic facies on state of the art CNN architectures

Dramsch, J. S., & Lüthje, M. (2018). Deep-learning seismic facies on state-of-the-art CNN architectures. In SEG Technical Program Expanded Abstracts 2018 (pp. 2036-2040). Society of Exploration Geophysicists.

  • Github: https://github.com/JesperDramsch/segam18

5: Complex-valued neural networks for machine learning on non-stationary physical data

Preprint: Dramsch, J. S., Lüthje, M., & Christensen, A. N. (2019). Complex-valued neural networks for machine learning on non-stationary physical data. arXiv preprint arXiv:1905.12321.

  • Github: https://github.com/JesperDramsch/Complex-CNN-Seismic

6: Machine Learning in 4D Seismic Inversion

Including Physics in Deep Learning – An Example from 4D Seismic Pressure Saturation Inversion

Dramsch, J. S., Corte, G., Amini, H., MacBeth, C., & Lüthje, M.. (2019). Including Physics in Deep Learning--An example from 4D seismic pressure saturation inversion. arXiv preprint arXiv:1904.02254.

  • Github: https://github.com/JesperDramsch/4D-seismic-neural-inversion

Deep Learning Application for 4D Pressure Saturation Inversion Compared to Bayesian Inversion on North Sea Data

Dramsch, J. S., Corte, G., Amini, H., Lüthje, M., & MacBeth, C.. (2019, April). Deep Learning Application for 4D Pressure Saturation Inversion Compared to Bayesian Inversion on North Sea Data. In Second EAGE Workshop Practical Reservoir Monitoring 2019.

  • Github: https://github.com/JesperDramsch/4D-seismic-neural-inversion

7: Deep Convolutional Networks for 4D Time Shift Extraction

Dramsch, J. S., Christensen, A. N., MacBeth, C., & Lüthje, M.. (2019, October 31). Deep Unsupervised 4D Seismic 3D Time-Shift Estimation with Convolutional Neural Networks. https://doi.org/10.31223/osf.io/82bnj - Github: https://github.com/JesperDramsch/voxelmorph-seismic

Appendix D

D.1: Information Theory Considerations in Patch-based Training of Deep Neural Networks on Seismic Time-Series

Dramsch, J. S., & Lüthje, M.. (2018, November). Information Theory Considerations in Patch-based Training of Deep Neural Networks on Seismic Time-Series. In First EAGE/PESGB Workshop Machine Learning.

  • Github: https://github.com/JesperDramsch/windowing-seismic-for-deep-learning

Appendix E

E.5: Software Manual: Keras Complex

Documentation PyPI Status PyPI Versions Build Status PyPI License

Dramsch, J. S., Trabelski, C., Bilaniuk, O., & Serdyuk, D.. (2019, September 7). Complex-Valued Neural Networks in Keras with Tensorflow (Version 3). figshare. doi:10.6084/m9.figshare.9783773.v3

  • Github: https://github.com/JesperDramsch/keras-complex