Chapters with code
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.
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.
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.
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.
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.
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
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.
Appendix E
E.5: Software Manual: Keras Complex
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