213 citations · 216 across the 2 of their papers we have counts for
4 papers
70 years of machine learning in geoscience in review
Jesper Sören Dramsch
This review gives an overview of the development of machine learning in geoscience. A thorough analysis of the co-developments of machine learning applications throughout the last…
Complex-valued neural networks for machine learning on non-stationary physical data
Jesper Sören Dramsch, Mikael Lüthje, Anders Nymark Christensen
Deep learning has become an area of interest in most scientific areas, including physical sciences. Modern networks apply real-valued transformations on the data. Particularly, con…
Including Physics in Deep Learning -- An example from 4D seismic pressure saturation inversion
Jesper Sören Dramsch, Gustavo Corte, Hamed Amini +2
Geoscience data often have to rely on strong priors in the face of uncertainty. Additionally, we often try to detect or model anomalous sparse data that can appear as an outlier in…
Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks
Lukas Mosser, Wouter Kimman, Jesper Dramsch +3
Traditional physics-based approaches to infer sub-surface properties such as full-waveform inversion or reflectivity inversion are time-consuming and computationally expensive. We…