A ConvLSTM Neural Network Model for Spatiotemporal Prediction of Mining Area Surface Deformation Based on SBAS-InSAR Monitoring Data

Author:

Yao Sheng1ORCID,He Yi1ORCID,Zhang Lifeng1ORCID,Yang Wang1ORCID,Chen Yi1ORCID,Sun Qiang1,Zhao Zhan'ao1,Cao Shengpeng1ORCID

Affiliation:

1. Faculty of Geomatics, the National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, and the Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou Jiaotong University, Lanzhou, China

Funder

Central Government to Guide Local Scientific and Technological Development

National Natural Science Foundation of China

Young Doctoral Fund Project of Higher Education Institutions in Gansu Province

Research Project of Transportation Department of Gansu Province

Key Research and Development (R&D) Programs—Industrial

Science and Technology Program of Gansu Province

Open Foundation of the Key Laboratory of Yellow River Environment of Gansu Province

Jiayuguan City 2021 Science and Technology Plan Projects

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Reference70 articles.

1. Surface subsidence monitoring of Jinchuan mining area in Gansu based on space–air–ground integration;li,2020

2. Study on the 3D deformation regularity of the open pit in Longshou mine based on time series InSAR Technique and GPS;ding;Metal Mine,2017

3. Bidirectional recurrent neural networks

4. Prediction of InSAR deformation time-series using a long short-term memory neural network

5. Prediction of InSAR time-series deformation using deep convolutional neural networks

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