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Detection of Volcanic Deformations in InSAR Velocity Maps - a contribution to TecVolSA project

Beker, Teo und Ansari, Homa und Montazeri, Sina und Song, Qian (2022) Detection of Volcanic Deformations in InSAR Velocity Maps - a contribution to TecVolSA project. Copernicus. EGU General Assembly 2022, 2022-05-23 - 2022-05-27, Vienna, Austria. doi: 10.5194/egusphere-egu22-7803.

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Kurzfassung

TecVolSA (Tectonics and Volcanoes in South America) is a project with a goal of developing intelligent Earth Observation (EO) data processing and exploitation for monitoring various geophysical processes in central south American Andes. Large amount of Sentinel-1 data over the period of about 5 years has been processed using mixed Permanent Scatterer and Distributed Scatterer (PS/DS) approaches. The received products are velocity maps with InSAR relative error in the order of 1 mm/yr on a large scale (>100km). The second milestone of the project was automatic extraction of information from the data. In this work, the focus is on detecting volcanic deformations. Since the real data prepared in such manner is limited, to train a deep learning model for detection of volcanic deformations, a synthetic training set is used. Models are trained from scratch and InceptionResNet v2 was selected for further experiments as it was found to givebest performance among the tested models. The explainable AI (XAI) techniques were used to understand and analyze the confidence of the model and to understand how to improve it. The models trained on synthetic training set underperformed on real test set. Using GradCAM technique, it was identified that slope induced signal and salt lake deformations were mistakenly identified as volcanic deformations. These patterns are difficult to simulate and were not contained in synthetic training set. Bridging this distribution gap was performed using hybrid synthetic-real fine-tuning set, consisting of the real slope induced signal data and synthetic volcanic data. Additionally, false positive rate of the model is reduced using low-pass spatial filtering of the real test set, and finally by adjustments of the temporal baseline received from a sensitivity analysis. The model successfully detected all 10 deforming volcanoes in the region, ranging from 0.4 - 1.8 cm/yr in deformation.

elib-URL des Eintrags:https://elib.dlr.de/186545/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Detection of Volcanic Deformations in InSAR Velocity Maps - a contribution to TecVolSA project
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Beker, TeoTeo.Beker (at) dlr.dehttps://orcid.org/0000-0003-1907-4045NICHT SPEZIFIZIERT
Ansari, HomaHoma.Ansari (at) dlr.dehttps://orcid.org/0000-0002-4549-2497NICHT SPEZIFIZIERT
Montazeri, SinaSina.Montazeri (at) dlr.dehttps://orcid.org/0000-0002-6732-1381NICHT SPEZIFIZIERT
Song, QianQian.Song (at) dlr.dehttps://orcid.org/0000-0003-2746-6858NICHT SPEZIFIZIERT
Datum:Mai 2022
Referierte Publikation:Nein
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
DOI:10.5194/egusphere-egu22-7803
Verlag:Copernicus
Status:veröffentlicht
Stichwörter:Deep Learning, Volcanic Deformations, InSAR deformation maps,
Veranstaltungstitel:EGU General Assembly 2022
Veranstaltungsort:Vienna, Austria
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:23 Mai 2022
Veranstaltungsende:27 Mai 2022
Veranstalter :Copernicus
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - SAR-Methoden, R - Künstliche Intelligenz
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > EO Data Science
Institut für Methodik der Fernerkundung > SAR-Signalverarbeitung
Hinterlegt von: Beker, Teo
Hinterlegt am:24 Mai 2022 13:46
Letzte Änderung:24 Apr 2024 20:47

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