Abstract
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.
Original language | English |
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Title of host publication | 81st EAGE Conference and Exhibition 2019 (Workshops) |
Publisher | European Association of Geoscientists and Engineers |
Publication date | 2019 |
Pages | 215-220 |
ISBN (Print) | 978-1-5108-9280-4 |
DOIs | |
Publication status | Published - 2019 |
Event | 81st EAGE Conference and Exhibition 2019 - ExCeL Centre, London, United Kingdom Duration: 3 Jun 2019 → 6 Jun 2019 Conference number: 81 |
Conference
Conference | 81st EAGE Conference and Exhibition 2019 |
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Number | 81 |
Location | ExCeL Centre |
Country/Territory | United Kingdom |
City | London |
Period | 03/06/2019 → 06/06/2019 |