A Deep-Learning Neural Network for Postseismic Deformation Reconstruction From InSAR Time Series
Author:
Affiliation:
1. Institute of Geology, China Earthquake Administration, Beijing, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/36/10354519/10500848.pdf?arnumber=10500848
Reference67 articles.
1. From Geodetic Imaging of Seismic and Aseismic Fault Slip to Dynamic Modeling of the Seismic Cycle
2. Space geodetic investigation of the coseismic and postseismic deformation due to the 2003Mw7.2 Altai earthquake: Implications for the local lithospheric rheology
3. Spatially variable fault friction derived from dynamic modeling of aseismic afterslip due to the 2004 Parkfield earthquake
4. Weak upper-mantle base revealed by postseismic deformation of a deep earthquake
5. Aseismic slip and recent ruptures of persistent asperities along the Alaska-Aleutian subduction zone
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