Global coseismic deformation dataset derived from InSAR

Fuente: Zenodo
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Autores principales: Yi, Yaning, Xu, Guangyu, Xu, Xiwei, Shi, Changjiang, Gao, Huiran, Li, Zhanfei
Formato: Recurso digital
Lenguaje:inglés
Publicado: Zenodo 2026
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author Yi, Yaning
Xu, Guangyu
Xu, Xiwei
Shi, Changjiang
Gao, Huiran
Li, Zhanfei
author_facet Yi, Yaning
Xu, Guangyu
Xu, Xiwei
Shi, Changjiang
Gao, Huiran
Li, Zhanfei
contents <p><span class="">This dataset consists of coseismic interferometric synthetic aperture radar (InSAR) interferograms.</span><span class=""> Each interferogram in this dataset visually represents the deformation field caused by an earthquake.</span><span class=""> The dataset is intended for researchers and scientists studying earthquake mechanisms, fault behavior, and crustal deformation.</span><span class=""> The interferograms are provided in common geospatial formats (tiff).</span></p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_20192351
institution Zenodo
language eng
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle Global coseismic deformation dataset derived from InSAR
Yi, Yaning
Xu, Guangyu
Xu, Xiwei
Shi, Changjiang
Gao, Huiran
Li, Zhanfei
Deep learning
Interferometric synthetic aperture radar
Convolutional neural network
Fast Fourier transform
Deformation extraction
<p><span class="">This dataset consists of coseismic interferometric synthetic aperture radar (InSAR) interferograms.</span><span class=""> Each interferogram in this dataset visually represents the deformation field caused by an earthquake.</span><span class=""> The dataset is intended for researchers and scientists studying earthquake mechanisms, fault behavior, and crustal deformation.</span><span class=""> The interferograms are provided in common geospatial formats (tiff).</span></p>
title Global coseismic deformation dataset derived from InSAR
topic Deep learning
Interferometric synthetic aperture radar
Convolutional neural network
Fast Fourier transform
Deformation extraction
url https://doi.org/10.5281/zenodo.20192351