10.6084/m9.figshare.8218421.v1
Jesper Soeren Dramsch
Jesper Soeren
Dramsch
Gustavo Corte
Gustavo
Corte
Hamed Amini
Hamed
Amini
Colin MacBeth
Colin
MacBeth
Mikael Lüthje
Mikael
Lüthje
Including Physics in Deep Learning - An example from 4D seismic pressure saturation inversion
figshare
2019
deep learning
neural network
residual physics
4D seismic
seismic inversion
inversion
variational autoencoder
Geophysics
2019-06-03 13:31:33
Presentation
https://figshare.com/articles/presentation/Including_Physics_in_Deep_Learning_-_An_example_from_4D_seismic_pressure_saturation_inversion/8218421
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 machine learning models. These are classic examples of imbalanced learning. Approaching these problems can benefit from including prior information from physics models or transforming data to a beneficial domain. We show an example of including physical information in the architecture of a neural network as prior information. We go on to present noise injection at training time to successfully transfer the network from synthetic data to field data.