| 70 years of machine learning in geoscience in review JS Dramsch Advances in geophysics 61, 1-55, 2020 | 166 | 2020 |
| Deep-learning seismic facies on state-of-the-art CNN architectures JS Dramsch, M Lüthje SEG International Exposition and Annual Meeting, SEG-2018-2996783, 2018 | 103 | 2018 |
| Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks L Mosser, W Kimman, J Dramsch, S Purves, A De la Fuente Briceño, ... 80th eage conference and exhibition 2018 2018 (1), 1-5, 2018 | 79 | 2018 |
| Bayesian convolutional neural networks for seismic facies classification R Feng, N Balling, D Grana, JS Dramsch, TM Hansen IEEE Transactions on Geoscience and Remote Sensing 59 (10), 8933-8940, 2021 | 50 | 2021 |
| Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets G Côrte, J Dramsch, H Amini, C MacBeth Geophysical Prospecting 68 (7), 2164-2185, 2020 | 33 | 2020 |
| Complex-valued neural networks for machine learning on non-stationary physical data JS Dramsch, M Lüthje, AN Christensen Computers & Geosciences 146, 104643, 2021 | 32 | 2021 |
| Outcomes of the WMO prize challenge to improve subseasonal to seasonal predictions using artificial intelligence F Vitart, AW Robertson, A Spring, F Pinault, R Roškar, W Cao, S Bech, ... Bulletin of the American Meteorological Society 103 (12), E2878-E2886, 2022 | 24 | 2022 |
| The rise of data-driven weather forecasting: A first statistical assessment of machine learning-based weather forecasts in an operational-like context Z Ben Bouallègue, MCA Clare, L Magnusson, E Gascon, M Maier-Gerber, ... Bulletin of the American Meteorological Society, 2024 | 19 | 2024 |
| An integrated workflow for fracture characterization in chalk reservoirs, applied to the Kraka Field TM Aabø, JS Dramsch, CL Würtzen, S Seyum, M Welch Marine and Petroleum Geology 112, 104065, 2020 | 15 | 2020 |
| Deep learning application for 4D pressure saturation inversion compared to Bayesian inversion on North Sea data JS Dramsch, G Corte, H Amini, M Lüthje, C MacBeth Second EAGE Workshop Practical Reservoir Monitoring 2019 2019 (1), 1-5, 2019 | 14 | 2019 |
| Deep unsupervised 4-D seismic 3-D time-shift estimation with convolutional neural networks JS Dramsch, AN Christensen, C MacBeth, M Lüthje IEEE Transactions on Geoscience and Remote Sensing 60, 1-16, 2021 | 12 | 2021 |
| Improving medium-range ensemble weather forecasts with hierarchical ensemble transformers ZB Bouallègue, JA Weyn, MCA Clare, J Dramsch, P Dueben, M Chantry Artificial Intelligence for the Earth Systems 3 (1), e230027, 2024 | 11 | 2024 |
| Including Physics in Deep Learning--An example from 4D seismic pressure saturation inversion JS Dramsch, G Corte, H Amini, C MacBeth, M Lüthje arXiv preprint arXiv:1904.02254, 2019 | 8 | 2019 |
| Machine Learning in 4D Seismic Data Analysis JS Dramsch Petroleum Geoscience 11 (2), 113-124, 2019 | 7 | 2019 |
| Correlation of Fractures From Core, Borehole Images and Seismic Data in a Chalk Reservoir in the Danish North Sea TM Aabø, JS Dramsch, MJ Welch, M Lüthje 79th EAGE Conference and Exhibition 2017 2017 (1), 1-5, 2017 | 3 | 2017 |
| Machine Learning in Geoscience. JS Dramsch parameters 1, 1ˆγik, 2019 | 2 | 2019 |
| Gaussian mixture models for robust unsupervised scanning-electron microscopy image segmentation of north sea chalk JS Dramsch, F Amour, M Lüthje First EAGE/PESGB Workshop Machine Learning 2018 (1), 1-3, 2018 | 2 | 2018 |
| Information theory considerations in patch-based training of deep neural networks on seismic time-series JS Dramsch, M Lüthje First EAGE/PESGB Workshop Machine Learning 2018 (1), 1-3, 2018 | 2 | 2018 |
| AIFS–ECMWF’s Data-Driven Probabilistic Forecasting ZB Bouallegue, M Alexe, M Chantry, M Clare, J Dramsch, S Lang, ... EGU24, 2024 | | 2024 |
| AIFS–ECMWF’s Data-Driven Probabilistic Forecasting System M Chantry, M Alexe, S Lang, B Raoult, J Dramsch, F Pinault, ... 104th AMS Annual Meeting, 2024 | | 2024 |
Mikael LuthjeVerified email at dtu.dk
