Generative Modeling of InSAR Interferograms
| Autor: | Thomas A. Herring, Guillaume Rongier, Victor Pankratius, Cody Rude |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Informatics
010504 meteorology & atmospheric sciences Computer science lcsh:Astronomy surface deformation Volcanology Technical Reports: Methods Geostatistics Environmental Science (miscellaneous) 010502 geochemistry & geophysics 01 natural sciences Volcano Monitoring Generative modeling Workflow lcsh:QB1-991 Tsunamis and Storm Surges InSAR Volcanic Hazards and Risks Interferometric synthetic aperture radar geostatistics Geodesy and Gravity Instruments and Techniques Seismology 0105 earth and related environmental sciences Remote sensing Ground truth Geological Effusive Volcanism generator lcsh:QE1-996.5 Landslide Avalanches Volcano Seismology Mud Volcanism lcsh:Geology machine learning Earthquake Ground Motions and Engineering Seismology Explosive Volcanism 13. Climate action General Earth and Planetary Sciences Noise (video) Cryosphere Mathematical Geophysics Natural Hazards Generator (mathematics) Oceanography: Physical |
| Zdroj: | Earth and Space Science (Hoboken, N.j.) Earth and Space Science, Vol 6, Iss 12, Pp 2671-2683 (2019) |
| ISSN: | 2333-5084 |
| Popis: | 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) applicationsThe tool leverages real data and geostatistical methods to generate and perturb interferogram componentsIt can be used to evaluate InSAR error correction workflows, to enhance machine learning use with InSAR, and to teach InSAR principles |
| Databáze: | OpenAIRE |
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