Generative Modeling of InSAR Interferograms

• Published in: Earth and space science (Hoboken, N.J.) N.J.), 2019-12, Vol.6 (12), p.2671-2683
• Main Authors: Rongier, Guillaume, Rude, Cody, Herring, Thomas, Pankratius, Victor
• Format: Article
• Language: English
• Subjects: Algorithms; Avalanches; Cryosphere; Deformation; Earthquake Ground Motions and Engineering Seismology; Earthquakes; Effusive Volcanism; Error correction & detection; Explosive Volcanism; generator; Geodesy and Gravity; Geological; geostatistics; Glaciers; Glaciohydrology; Informatics; InSAR; Instruments and Techniques; Ionosphere; Landslides; Machine learning; Mathematical Geophysics; Mud Volcanism; Natural Hazards; Noise; Oceanography: Physical; Seismic activity; Seismology; surface deformation; Surface runoff; Technical Reports: Methods; Topography; Tsunamis and Storm Surges; Values; Volcanic Hazards and Risks; Volcano Monitoring; Volcano Seismology; Volcanoes; Volcanology; Workflow
• ISSN: 2333-5084; 2333-5084
• DOI: 10.1029/2018EA000533

Interferometric synthetic aperture radar (InSAR) has become an essential technique to detect surface variations due to volcanoes, earthquakes, landslides, glaciers, and aquifers. However, Earth's ionosphere, atmosphere, vegetation, surface runoff, etc., introduce noise that requires post‐processing to separate its components. This work defines a generator to create interferograms that include each of those components. Our approach leverages deformation models with real data, either directly or through machine learning using geostatistical methods. These methods result from previous developments to more efficiently and better simulate spatial variables and could replace some statistical approaches used in InSAR processing. We illustrate the use of the generator to simulate an artificial interferogram based on the 2015 Illapel earthquake and discuss the improved performance offered by geostatistical approaches compared with classical statistical ones. The generator establishes a tool for multiple applications (1) to evaluate InSAR correction workflows in controlled scenarios with known ground truth; (2) to develop training sets and generative methods for machine learning algorithms; and (3) to educate on InSAR and its principles. Key Points We introduce a software tool that can generate artificial interferograms for synthetic aperture radar (SAR) applications The tool leverages real data and geostatistical methods to generate and perturb interferogram components It can be used to evaluate InSAR error correction workflows, to enhance machine learning use with InSAR, and to teach InSAR principles