ScientistForecast Department, Innovation Platform Teampage tabs Profile Summary: Jesper Dramsch implements state-of-the-art machine learning solutions for numerical weather prediction, approaching different topics in the 10 years ML roadmap at ECMWF. Professional interests: Machine learning in science Validation of machine learning models in real-world contexts Systems thinking Semi-supervised and unsupervised learning Career background: Work Experience 2021 - present: Scientist for Machine Learning, ECMWF 2020 - 2021: Machine Learning Engineer, GMV NSL 2019 - 2020: Postdoc, Technical University of Denmark 2019 - 2020: Machine Learning and Python educator, Agile* 2018 - 2019: Visiting Scholar, Heriot-Watt University Edinburgh 2016 - 2019: PhD, Technical University of Denmark 2016 - 2016: Research Assistant, Technical University of Denmark 2016 - 2016: Research Assistant, GfZ Potsdam 2014 - 2015: Geotechnical Student Assistant, O+P Geotechnik 2013 - 2014: Lab Assistant, DESY 2012 - 2012: Geophysics Intern, Schlumberger 2011 - 2011: Geophysics Intern, Fugro FSI 2010 - 2012: Student Research Assistant, University of Hamburg 2007 - 2007: Intern Depth Imaging, GfZ Potsdam Open Source Contributions Maintainer Open Geoscience Awesome List (Former) Keras Complex DOCUMENTATION Tensorflow Scikit-Learn Pandas Bruges Publications 2022Vitart F, A.W. Robertson, A. Spring, F. Pinault, R. Roškar, W. Cao, S. Bech, A. Bienkowski, N. Caltabiano, E. De Coning, B. Denis, A. Dirkson, Jesper Sören Dramsch, P. Dueben, J. Gierschendorf, H. S. Kim, K. Nowak, D. Landry, L. Lledó, L. Palma, S. Rasp, S. Zhou (September 2022) Outcomes of the WMO Prize Challenge to Improve Sub-Seasonal to Seasonal Predictions Using Artificial Intelligence, Bulletin of the American Meteorological Society. DOI: 10.1175/bams-d-22-0046.1 Jesper Soren Dramsch, Anders Nymark Christensen, Colin MacBeth, Mikael Luthje (2022) Deep Unsupervised 4-D Seismic 3-D Time-Shift Estimation With Convolutional Neural Networks, IEEE Transactions on Geoscience and Remote Sensing, pp. 1–16. DOI: 10.1109/TGRS.2021.3081516 2021Jesper Sören Dramsch, Mikael Lüthje, Anders Nymark Christensen (January 2021) Complex-valued neural networks for machine learning on non-stationary physical data, Computers & Geosciences, pp. 104643. DOI: 10.1016/j.cageo.2020.104643 Runhai Feng, Niels Balling, Dario Grana, Jesper Soren Dramsch, Thomas Mejer Hansen (October 2021) Bayesian Convolutional Neural Networks for Seismic Facies Classification, IEEE Transactions on Geoscience and Remote Sensing n. 10, pp. 8933–8940. DOI: 10.1109/TGRS.2020.3049012 2020Tala Maria Aabø, Jesper Sören Dramsch, Camilla Louise Würtzen, Solomon Seyum, Michael Welch (February 2020) An integrated workflow for fracture characterization in chalk reservoirs, applied to the Kraka Field, Marine and Petroleum Geology, pp. 104065. DOI: 10.1016/j.marpetgeo.2019.104065 (June 2020) 70 years of machine learning in geoscience in review. Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v4 Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v6 Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v7 Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v8 Gustavo Côrte, Jesper Dramsch, Hamed Amini, Colin MacBeth (September 2020) Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets, Geophysical Prospecting. DOI: 10.1111/1365-2478.12982 Jesper Sören Dramsch (2020) 3D decision volume of SVM, Random Forest, and Deep Neural Network, figshare. DOI: 10.6084/m9.figshare.12640226.v1 Jesper Sören Dramsch (2020) 3D decision volume of SVM, Random Forest, and Deep Neural Network, figshare. DOI: 10.6084/m9.figshare.12640226 Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v5 Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v9 2019Jesper Sören Dramsch (November 2019) Trace Interpolation with Partial CRS-Stacks. DOI: 10.31237/osf.io/mvxuh Jesper Sören Dramsch (November 2019) Seismic Subsalt Imaging with Prestack Data Enhancement Methods. DOI: 10.31237/osf.io/aec7p Jesper Dramsch, Gustavo Corte, Hamed Amini, Mikael Lüthje, Colin MacBeth (February 2019) Deep Learning Application for 4D Pressure Saturation Inversion Compared to Bayesian Inversion on North Sea Data. DOI: 10.31223/OSF.IO/ZYTP2 Jesper Sören Dramsch, Gustavo Corte, Hamed Amini, Mikael Luthje, Colin Macbeth (2019) Deep Learning Application for 4D Pressure Saturation Inversion Compared to Bayesian Inversion on North Sea Data, Proceedings of the Second EAGE Workshop Practical Reservoir Monitoring. DOI: 10.31223/osf.io/zytp2 Jesper Sören Dramsch, Anders Nymark Christensen, Mikael Lüthje (2019) Physics and Deep Learning - Incorporating prior knowledge in deep neural networks, Figshare. DOI: 10.6084/m9.figshare.8217518 Jesper Sören Dramsch, Gustavo Corte, Hamed Amini, Colin MacBeth, Mikael Lüthje (2019) Including Physics in Deep Learning - An example from 4D seismic pressure saturation inversion, Figshare. DOI: 10.6084/m9.figshare.8218421 Jesper Sören Dramsch, G. Corte, H. Amini, C. Macbeth, Mikael Luthje (2019) Including Physics in Deep Learning – An Example from 4D Seismic Pressure Saturation Inversion, 81st EAGE Conference and Exhibition 2019 (Workshops), pp. 215–220. DOI: 10.3997/2214-4609.201901967 Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (2019) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v3 Jesper Dramsch, Anders Christensen, Colin MacBeth, Mikael Lüthje (October 2019) Deep Unsupervised 4D Seismic 3D Time-Shift Estimation with Convolutional Neural Networks. DOI: 10.31223/OSF.IO/82BNJ Jesper Sören Dramsch (2019) Machine Learning in 4D Seismic Data Analysis: Deep Neural Networks in Geophysics. 2018 (May 2018) Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks, ArXiv e-prints. DOI: 10.3997/2214-4609.201800734 Jesper Sören Dramsch, Mikail Baykulov, Dirk Gajewski (2018) Trace inteprolation with partial CRS stack, Figshare. DOI: 10.6084/M9.FIGSHARE.6958529 Jesper Sören Dramsch (2018) KFold in Deep Learning Lightning Talk, Figshare. DOI: 10.6084/M9.FIGSHARE.7035908 Jesper S. Dramsch, Mikael Lüthje (August 2018) Deep-learning seismic facies on state-of-the-art CNN architectures, SEG Technical Program Expanded Abstracts 2018. DOI: 10.1190/segam2018-2996783.1 Jesper Sören Dramsch, Mikael Lüthje (2018) Deep-learning seismic facies on state-of-the-art CNN architectures, Figshare. DOI: 10.6084/m9.figshare.7301645.v1 Jesper Sören Dramsch (2018) A practitioner's guide to deep learning in geophysical imaging, Figshare. DOI: 10.6084/m9.figshare.7170299 Lukas Mosser, Wouter Kimman, Jesper Sören Dramsch, Steve Purves, Alfredo De la Fuente, Graham Ganssle (2018) Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks, Figshare. DOI: 10.6084/m9.figshare.6958517.v1 Jesper Sören Dramsch, Mikail Baykulov, Dirk Gajewski (2018) Trace inteprolation with partial CRS stack, Figshare. DOI: 10.6084/m9.figshare.6958529.v1 Jesper Sören Dramsch (2018) A practitioner's guide to deep learning in geophysical imaging, Figshare. DOI: 10.6084/m9.figshare.7170299.v1 J.S. Dramsch, M. Lüthje (November 2018) Information Theory Considerations In Patch-Based Training Of Deep Neural Networks On Seismic Time-Series, First EAGE/PESGB Workshop Machine Learning. DOI: 10.3997/2214-4609.201803020 J.S. Dramsch, F. Amour, M. Lüthje (November 2018) Gaussian Mixture Models For Robust Unsupervised Scanning-Electron Microscopy Image Segmentation Of North Sea Chalk, First EAGE/PESGB Workshop Machine Learning. DOI: 10.3997/2214-4609.201803014 Jesper Sören Dramsch, Mikael Lüthje (2018) Deep Learning: From Cats to 4D Seismic - Reducing cycle time and model training cost in asset management, Figshare. DOI: 10.6084/m9.figshare.7422629 2017T.M. Aabø, J.S. Dramsch, M.J. Welch, M. Lüthje (June 2017) Correlation of Fractures From Core, Borehole Images and Seismic Data in a Chalk Reservoir in the Danish North Sea, 79th EAGE Conference and Exhibition 2017. DOI: 10.3997/2214-4609.201701283 T.M. Aabø, M.J. Welch, Jesper Sören Dramsch, Mikael Luthje, S. Seyum, Frédéric Amour, C.L. Würtzen (2017) Fracture Characterization and Modelling in the Kraka Field, Danish Hydrocarbon Research and Technology Centre Technology Conference 2017, Lyngby, Denmark, 14/11/2017. 2011J. S. Dramsch, D. J. Gajewski (May 2011) Trace Interpolation with Partial CRS Stacks, 73rd EAGE Conference and Exhibition incorporating SPE EUROPEC 2011. DOI: 10.3997/2214-4609.20149421
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