Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

A deep neural network is developed to automatically extract ground deformation from Interferometric Synthetic Aperture Radar time series. Applied to data over the North Anatolian Fault, the method can detect 2 mm deformation transients and reveals a slow earthquake twice as extensive as previously r...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Bertrand Rouet-Leduc, Romain Jolivet, Manon Dalaison, Paul A. Johnson, Claudia Hulbert
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/65a14e05f4c846dcb44ba44718e995af
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:65a14e05f4c846dcb44ba44718e995af
record_format	dspace
spelling	oai:doaj.org-article:65a14e05f4c846dcb44ba44718e995af2021-11-14T12:34:33ZAutonomous extraction of millimeter-scale deformation in InSAR time series using deep learning10.1038/s41467-021-26254-32041-1723https://doaj.org/article/65a14e05f4c846dcb44ba44718e995af2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-26254-3https://doaj.org/toc/2041-1723A deep neural network is developed to automatically extract ground deformation from Interferometric Synthetic Aperture Radar time series. Applied to data over the North Anatolian Fault, the method can detect 2 mm deformation transients and reveals a slow earthquake twice as extensive as previously recognized.Bertrand Rouet-LeducRomain JolivetManon DalaisonPaul A. JohnsonClaudia HulbertNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Bertrand Rouet-Leduc Romain Jolivet Manon Dalaison Paul A. Johnson Claudia Hulbert Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning
description	A deep neural network is developed to automatically extract ground deformation from Interferometric Synthetic Aperture Radar time series. Applied to data over the North Anatolian Fault, the method can detect 2 mm deformation transients and reveals a slow earthquake twice as extensive as previously recognized.
format	article
author	Bertrand Rouet-Leduc Romain Jolivet Manon Dalaison Paul A. Johnson Claudia Hulbert
author_facet	Bertrand Rouet-Leduc Romain Jolivet Manon Dalaison Paul A. Johnson Claudia Hulbert
author_sort	Bertrand Rouet-Leduc
title	Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning
title_short	Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning
title_full	Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning
title_fullStr	Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning
title_full_unstemmed	Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning
title_sort	autonomous extraction of millimeter-scale deformation in insar time series using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/65a14e05f4c846dcb44ba44718e995af
work_keys_str_mv	AT bertrandrouetleduc autonomousextractionofmillimeterscaledeformationininsartimeseriesusingdeeplearning AT romainjolivet autonomousextractionofmillimeterscaledeformationininsartimeseriesusingdeeplearning AT manondalaison autonomousextractionofmillimeterscaledeformationininsartimeseriesusingdeeplearning AT paulajohnson autonomousextractionofmillimeterscaledeformationininsartimeseriesusingdeeplearning AT claudiahulbert autonomousextractionofmillimeterscaledeformationininsartimeseriesusingdeeplearning
_version_	1718429110160588800

Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

Ejemplares similares