Deep Learning-Based Coseismic Deformation Estimation From InSAR Interferograms

Chuanhua Zhu

doi:10.1109/TGRS.2024.3357190

2024-01-01

PRE

DOI：10.1109/TGRS.2024.3357190

原文链接

摘要

En 中文

Accurate automated extraction of coseismic deformation from synthetic aperture radar (SAR) data can be challenging owing to interference from inherent atmospheric noise. Particularly, the limited displacement of small-to-moderate earthquakes (Mw < 6.5) can easily be obscured by phase errors and/or noise. To address this issue, we developed an autoencoder model based on a deep learning framework (i.e., Pytorch) to automate the accurate extraction of coseismic displacement from interferometric SAR (InSAR) interferograms. We constructed a training dataset using simulated interferograms. Our trained model performed well for interferograms with real noise. When applied to worldwide real earthquakes of various rupture styles, the model produced clear coseismic displacement with less noise and a better fit to coseismic fault models compared to the differential InSAR (D-InSAR) method without noise correction. Additionally, it achieved co-seismic deformation similar to popular InSAR time series and Global Navigation Satellite System (GNSS) methods. The approach will enhance the proceduralization and popularization of InSAR applications in earthquake monitoring, providing improved constraints on the kinematic characteristics of earthquakes.

Keyword:

Autoencoder

coseismic displacement

deep learning

error removal

time-series interferometric synthetic aperture radar (InSAR)

期刊

IEEE Transactions on Geoscience and Remote Sensing

IF:

8.6

论文数:

2.2W

被引数:

10.7W

机构

China Earthquake Administration

学者数:

5.2K

论文数: 3.2K

被引数: 2.5K

institute of geology, cea

学者数:

506

论文数: 429

被引数: 0

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