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  TecVolSA: InSAR and Machine Learning for Surface Displacement Monitoring in South America

Montazeri, S., Ansari, H., De Zan, F., Mania, R., Shau, R., Beker, T., Parizzi, A., Haghshenas Haghighi, M., Niemz, P., Cesca, S., Motagh, M., Walter, T., Eineder, M., Zhu, X. X. (2021): TecVolSA: InSAR and Machine Learning for Surface Displacement Monitoring in South America - Abstracts, EGU General Assembly 2021 (Online 2021).

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Montazeri, Sina1, Author
Ansari, Homa1, Author
De Zan, Francesco1, Author
Mania, R.2, Author              
Shau, Robert1, Author
Beker, Teo1, Author
Parizzi, Alessandro1, Author
Haghshenas Haghighi, Mahmud3, Author              
Niemz, P.2, Author              
Cesca, Simone2, Author              
Motagh, M.3, Author              
Walter, Thomas2, Author              
Eineder, Michael1, Author
Zhu, Xiao Xiang1, Author
1External Organizations, ou_persistent22              
22.1 Physics of Earthquakes and Volcanoes, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146029              
31.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146028              


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 Abstract: TecVolSA (Tectonics and Volcanoes in South America) is a project dedicated to the development of an intelligent Earth Observation (EO) data exploitation system for monitoring various geophysical activities in South America. Three partners from the German Aerospace Center (DLR) and the German Research Centre for Geosciences (GFZ) are involved to combine their expertise in signal processing, geophysics and Artificial Intelligence (AI). The first milestone of the project is to perform interferometric processing on tens of terabytes of SAR data to generate deformation products. Efficient algorithms have been designed to accommodate big data processing. Employing these algorithms, five-year data archives of Sentinel-1 have been processed thus far. The data archives span an area of over 770,000 km² surrounding the central volcanic zone of the Andes. Products in the form of surface deformation velocity and displacement time series are generated as point-wise measurements. To ensure highly accurate deformation estimates, two novel techniques have been utilized: large-scale atmospheric correction and covariance-based phase estimation for distributed scatterers. The second milestone is automatic mining of the wealth of the deformation products to gain insights about anthropogenic and geophysical signals in the region. Here two challenges are faced: the variety of crustal deformation processes as well as the sheer volume of the data. A closer analysis of the estimated deformation velocity verifies the presence of various signals including tectonic movements, volcanic unrest and slope-induced deformations. Such variety requires the classification of the observed signals. Furthermore, the dataset includes displacement time series and velocity estimates of over 750 million data points. This data volume necessitates the incorporation of AI for efficient mining of the products. The aforementioned challenges are met by combining geophysical and signal processing expertise of the project partners, and translating them to the AI algorithms. The use of AI in EO is a growing topic with numerous successful applications. However, compared to the well-established AI applications of cartography and ground cover classification, there is not enough training data available for the analysis of tectonic and volcanic signals. Therefore, there is a need for synthetic data generation. GFZ produces geophysical models for the simulation of a diverse database that is used for the training of neural networks to autonomously discover significant events in deformation products. DLR employs supervised machine learning techniques based on simulated data to automatically detect volcanic deformation from InSAR products. Apart from this application, signals which are not attributed to volcanic deformation are automatically clustered for further studies by expert geologists. For this approach, we depend on InSAR and geometrical feature engineering as well as advanced unsupervised learning algorithms. In the presentation, examples of clustering similar points in terms of temporal progression and a prototype system for the automatic detection of volcanic deformations will be illustrated. Our system is being developed with scalability and transferability in mind. South America serves as a generic and challenging case for this development, as it reveals manifold geophysical and anthropogenic signals. Our ultimate goal is to apply the developed AI-assisted system for global processing.


Language(s): eng - English
 Dates: 20212021
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: GFZPOF: p4 T3 Restless Earth
eDoc: 10.5194/egusphere-egu21-6086
 Degree: -


Title: EGU General Assembly 2021
Place of Event: Online
Start-/End Date: 2021-04-19 - 2021-04-30

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Title: Abstracts
Source Genre: Proceedings
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