Jesper Sören Dramsch
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70 years of machine learning in geoscience in review
JS Dramsch
Advances in geophysics 61, 1-55, 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
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
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
Complex-valued neural networks for machine learning on non-stationary physical data
JS Dramsch, M Lüthje, AN Christensen
Computers & Geosciences 146, 104643, 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
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
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
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
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
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
Machine Learning in 4D Seismic Data Analysis
JS Dramsch
Petroleum Geoscience 11 (2), 113-124, 2019
The rise of data-driven weather forecasting
Z Ben-Bouallegue, MCA Clare, L Magnusson, E Gascon, M Maier-Gerber, ...
arXiv preprint arXiv:2307.10128, 2023
Improving medium-range ensemble weather forecasts with hierarchical ensemble transformers
Z Ben-Bouallegue, JA Weyn, MCA Clare, J Dramsch, P Dueben, ...
arXiv preprint arXiv:2303.17195, 2023
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
Machine Learning in Geoscience
JS Dramsch
parameters 1, 1ˆγik, 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
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
Keynote 5: Informing neural networks with fluid flow consistent property correlations: A 4D seismic inversion application
G Corte, J Dramsch, H Amini, C MacBeth
EAGE GeoTech 2021 Third EAGE Workshop on Practical Reservoir Monitoring 2021 …, 2021
Deep Neural Network Application for 4D Seismic Inversion to Pressure and Saturation: Enhancing Training Data Sets
G Corte, J Dramsch, C MacBeth, H Amini
EAGE 2020 Annual Conference & Exhibition Online 2020 (1), 1-5, 2020
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