Including Physics in Deep Learning – An Example from 4D Seismic Pressure Saturation Inversion
J.S. Dramsch, G. Corte, H. Amini, C. MacBeth and M. Lüthje
Event name: 81st EAGE Conference and Exhibition 2019 Workshop Programme
Session: WS10 Machine Learning: Opportunities and Challenges
Publication date: 03 June 2019
Info: Extended abstract, PDF ( 545.15Kb )
Price: € 20
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.