Time-Series Analysis and Prediction of Surface Deformation in the Jinchuan Mining Area, Gansu Province, by Using InSAR and CNN–PhLSTM Network

干涉合成孔径雷达 计算机科学 卷积神经网络 时间序列 变形(气象学) 人工智能 合成孔径雷达 支持向量机 系列(地层学) 数据挖掘 地质学 模式识别(心理学) 遥感 机器学习 古生物学 海洋学
作者
Yi He,Haowen Yan,Yang Wang,Sheng Yao,Lifeng Zhang,Yi Chen,Tao Liu
出处
期刊:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:15: 6732-6751 被引量:21
标识
DOI:10.1109/jstars.2022.3198728
摘要

Surface deformation poses a great threat to the safety of Jinchuan mining area production activities. At present, the spatio-temporal evolution law and mechanism of surface deformation in the Jinchuan mining area are unclear, and it is difficult to obtain reliable prediction results using the existing spatio-temporal prediction methods due to the lack of model parameters or relevant data. To solve these problems, this study proposes a new unified convolutional neural network with peephole long short-term memory (CNN-PhLSTM). Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used to obtain the spatio-temporal evolution characteristics of surface deformation in the period of 2014–2021. Time series InSAR deformation data are merged into a unified network model in series with a time-distributed CNN segmentation and stacked PhLSTM. The InSAR measurement results are shown to be reliable by comparison and verification with the benchmark and InSAR results of different orbits. The proposed CNN-PhLSTM model was evaluated by mean absolute error and structural similarity (SSIM) evaluation indexes, and was compared with support vector regression (SVR), multilayer perceptron (MLP) and CNN-LSTM models. The results show three continuous subsidence areas, namely the Longshou, second western and third eastern mining areas. The cumulative surface deformation continued to increase from 2014 to 2021. Faults and lithology control the spatial distribution of surface deformation in the Jinchuan mining area. The prediction results demonstrate that the surface deformation range will continue to expand and that time-series surface deformation will show a slow deceleration trend in the next two years.
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