InSAR time-series deformation forecasting surrounding Salt Lake using deep transformer models

干涉合成孔径雷达 变形(气象学) 永久冻土 变形监测 地质学 时间序列 大地测量学 遥感 合成孔径雷达 计算机科学 机器学习 海洋学
作者
Jing Wang,Chao Li,Lü Li,Zhihua Huang,Chao Wang,Hong Zhang,Zhengjia Zhang
出处
期刊:Science of The Total Environment [Elsevier]
卷期号:858: 159744-159744 被引量:10
标识
DOI:10.1016/j.scitotenv.2022.159744
摘要

The free and open data policy of Sentinel-1 SAR images enables Radar interferometry (InSAR) to perform time series surface deformation monitoring over large areas. InSAR deformation monitoring and prediction can investigate the freeze-thaw cycles of permafrost on the Qinghai-Tibet Plateau. However, the convolutional and recurrent neural networks cannot accurately model long-term and complex relations in multivariate time series data, it is challenging to implement time series deformation prediction with high spatial resolution. In this paper, an innovative InSAR deformation prediction integrated algorithm based on the transformer models is proposed to predict time series deformation more accurately surrounding Salt Lake. Compared with the other solutions, the unique feature of the proposed method is that: 1) this method takes advantage of the self-attention mechanism to study complicated dynamic deformation features of permafrost caused by temperature and other variables from InSAR time series deformation. 2) The transformer-based model can more accurately simulate seasonal and non-seasonal deformation signals, and is effective for short-term prediction of surface deformation in permafrost areas. The InSAR deformation prediction results demonstrate that the InSAR deformation prediction method achieves better prediction performance in predicting the deformation trends of permafrost with a point scale compared with the prediction results of other models. Based on the predicted deformation and the water extraction results, the expansion trends surrounding Salt Lake are discussed and evaluated. The total area of Salt Lake increased by 57.32 km2 during the period 2015-2019. And Salt Lake maintained slowing expansion trend from 2019 to 2022. The time series deformation forecasting method can be used as a generic framework for modeling nonlinear deformation processes in complex permafrost areas, and it reveals the potential impact of the Salt Lake outburst event on the deformation processes and the degradation of permafrost.
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