Generative Modeling of InSAR Interferograms
Abstract 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 po...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2019-12-01
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| Series: | Earth and Space Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2018EA000533 |
| _version_ | 1818838547843514368 |
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| author | Guillaume Rongier Cody Rude Thomas Herring Victor Pankratius |
| author_facet | Guillaume Rongier Cody Rude Thomas Herring Victor Pankratius |
| author_sort | Guillaume Rongier |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-19T03:40:08Z |
| format | Article |
| id | doaj.art-e4c58809c7c1413184554cff31d42cd9 |
| institution | Directory Open Access Journal |
| issn | 2333-5084 |
| language | English |
| last_indexed | 2024-12-19T03:40:08Z |
| publishDate | 2019-12-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj.art-e4c58809c7c1413184554cff31d42cd92022-12-21T20:37:15ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842019-12-016122671268310.1029/2018EA000533Generative Modeling of InSAR InterferogramsGuillaume Rongier0Cody Rude1Thomas Herring2Victor Pankratius3Department of Earth, Atmospheric, and Planetary Sciences Massachusetts Institute of Technology Cambridge MA USADepartment of Earth, Atmospheric, and Planetary Sciences Massachusetts Institute of Technology Cambridge MA USADepartment of Earth, Atmospheric, and Planetary Sciences Massachusetts Institute of Technology Cambridge MA USAKavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology Cambridge MA USAAbstract 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.https://doi.org/10.1029/2018EA000533InSARsurface deformationgeneratormachine learninggeostatistics |
| spellingShingle | Guillaume Rongier Cody Rude Thomas Herring Victor Pankratius Generative Modeling of InSAR Interferograms Earth and Space Science InSAR surface deformation generator machine learning geostatistics |
| title | Generative Modeling of InSAR Interferograms |
| title_full | Generative Modeling of InSAR Interferograms |
| title_fullStr | Generative Modeling of InSAR Interferograms |
| title_full_unstemmed | Generative Modeling of InSAR Interferograms |
| title_short | Generative Modeling of InSAR Interferograms |
| title_sort | generative modeling of insar interferograms |
| topic | InSAR surface deformation generator machine learning geostatistics |
| url | https://doi.org/10.1029/2018EA000533 |
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