结合Prophet-CNN模型的多轨时序InSAR矿区三维形变监测预警

doi:10.13474/j.cnki.11-2246.2024.0510

测绘通报 ›› 2024, Vol. 0 ›› Issue (5): 53-59.doi: 10.13474/j.cnki.11-2246.2024.0510

• 学术研究 • 上一篇

结合Prophet-CNN模型的多轨时序InSAR矿区三维形变监测预警

毕自航¹, 李素敏^1,2,3, 张龙宇¹, 张玮⁴, 李袁松⁴, 袁利伟⁵

1. 昆明理工大学国土资源工程学院, 云南昆明 650093;
2. 云南省高校高原山区空间信息测绘技术应用工程研究中心, 云南昆明 650093;
3. 中国有色金属工业协会智慧矿山地理空间信息集成创新重点实验室, 云南昆明 650093;
4. 玉溪大红山矿业有限公司, 云南玉溪 653400;
5. 昆明理工大学公共安全与应急管理学院, 云南昆明 650093

收稿日期:2023-10-07 发布日期:2024-06-12
通讯作者: 李素敏。E-mail:153064487@qq.com
作者简介:毕自航(1995—),男,硕士生,研究方向为InSAR三维形变监测及矿山边坡失稳模式。E-mail:971363270@qq.com
基金资助:
国家自然科学基金(52364020;42161067);云南省基础研究计划(202301AT070463)

Multi-track sequential InSAR mining 3D deformation monitoring and prediction combined with Prophet-CNN model

BI Zihang¹, LI Sumin^1,2,3, ZHANG Longyu¹, ZHANG Wei⁴, LI Yuansong⁴, YUAN Liwei⁵

1. Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China;
2. Engineering Research Center for Application of Spatial Information Surveying and Mapping Technology in Plateau Mountain Area, Kunming 650093, China;
3. Key Laboratory of Smart Mine Geospatial Information Integration Innovation, China Nonferrous Metals Industry Association, Kunming 650093, China;
4. Yuxi Da hong shan Mining Co., Ltd., Yuxi 653400, China;
5. School of Public Safety and Emergency Management, Kunming University of Science and Technology, Kunming 650093, China

Received:2023-10-07 Published:2024-06-12

摘要/Abstract

摘要： 绝大多数矿区由于矿床的地质条件及特殊的采矿方式,推进开采容易引发矿区地质应力失衡,造成不同程度的变形破坏。为探究采矿活动下大红山矿区地表稳定性,本文通过对覆盖大红山矿区2021—2023年的三轨SAR影像进行联合解算矿区三维形变场,分析矿区形变特征;在此基础上,运用Prophet-CNN模型对形变时序进行训练,构建形变预测模型对矿区的三维形变趋势并进行预测;结果表明,矿区在开采活动的影响下地表持续发生形变,形变主要分布于硝水箐—南部废石场、露天采区及铜矿采区,最大垂直形变速率为-51.22mm/a;采用Prophet-CNN组合模型对矿区地表三维形变进行时序预测,3个方向预测结果的RMSE与MAE分别在2.90和1.85mm以下,充分证明了本文方法能够运用于矿区沉降趋势预测,为防灾减灾工作提供技术依据。

关键词: InSAR, 升降轨, 三维形变分解, Prophet-CNN, 监测预测

Abstract: Due to the geological conditions and special mining methods of the mineral deposits, most mining areas are prone to cause the imbalance of geological stress in mining areas, resulting in different degrees of deformation and failure. In order to explore the surface stability of Dahongshan mining area under mining activities, the three-dimensional deformation field of Dahongshan mining area is calculated by combining the three-track SAR images covering the area from 2021 to 2023, and the deformation characteristics of the mining area are analyzed. On this basis, the Prophet-CNN model is used to train the deformation time series, and the deformation prediction model is built to predict the three-dimensional deformation trend of the mining area. The results show that the surface deformation occurs continuously under the influence of mining activities, and the deformation is mainly distributed in Xiaoshuiqing southern waste rock field, open-pit mining area and copper mining area, and the maximum vertical deformation rate is -51.22mm/a. The Prophet-CNN combined model is used to predict the 3D surface deformation in the mining area in time series, and the RMSE and MAE of the prediction results in the three directions are below 2.90 and 1.85mm, respectively, which fully indicated that the proposed method could be applied to the prediction of the subsidence trend in the mining area and provide technical basis for disaster prevention and reduction.

Key words: InSAR, ascending and descending, 3D deformation decomposition, Prophet-CNN, monitoring and prediction

中图分类号:

P237

毕自航, 李素敏, 张龙宇, 张玮, 李袁松, 袁利伟. 结合Prophet-CNN模型的多轨时序InSAR矿区三维形变监测预警[J]. 测绘通报, 2024, 0(5): 53-59.

BI Zihang, LI Sumin, ZHANG Longyu, ZHANG Wei, LI Yuansong, YUAN Liwei. Multi-track sequential InSAR mining 3D deformation monitoring and prediction combined with Prophet-CNN model[J]. Bulletin of Surveying and Mapping, 2024, 0(5): 53-59.

参考文献

[1] BAR N,DIXON R. Unveiling unknowns: practical application of InSAR for slope performance monitoring and risk management across multiple surface mines[J]. Engineering Geology,2021,293: 106326.
[2] 汪云甲. 矿区生态扰动监测研究进展与展望[J]. 测绘学报,2017,46(10): 1705-1716.
[3] 贺黎明,裴攀科,吴立新,等. 基于时序InSAR的矿区滑坡前地表运动特征分析[J]. 东北大学学报(自然科学版),2022,43(9): 1314-1321.
[4] 何荣,刘畅远,徐可心.基于SBAS-InSAR的大采深条带开采地表沉降监测与特征分析[J].金属矿山,2021(2):16-22.
[5] 朱建军,李志伟,胡俊. InSAR变形监测方法与研究进展[J]. 测绘学报,2017,46(10): 1717-1733.
[6] 杨志法,陈剑. 关于滑坡预测预报方法的思考[J]. 工程地质学报,2004,12(2): 118-123.
[7] 周文韬,张文君,杨元继,等. 矿区地表沉降监测的一种组合模型预测方法[J]. 大地测量与地球动力学,2021,41(3): 308-312.
[8] HANSSEN R.Radar interferometry:data interpretation and error analysis[M].[S.l]:Radar Interferometry,2001.
[9] YU Chen,LI Zhenhong,PENNA N T.Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model[J].Remote Sensing of Environment,2018,204:109-121.
[10] YU Chen,LI Zhenhong,PENNA N T,et al.Generic atmospheric correction model for interferometric synthetic aperture radar observations[J].Journal of Geophysical Research:Solid Earth,2018,123(10):9202-9222.
[11] 朱建军,杨泽发,李志伟.InSAR矿区地表三维形变监测与预计研究进展[J].测绘学报,2019,48(2):135-144.
[12] 胡俊. InSAR监测地表三维形变的研究[D]. 长沙:中南大学,2008.
[13] TAYLOR S J,LETHAM B. Forecasting at scale[J]. The American Statistician,2018,72(1): 37-45.
[14] LI Zewen,LIU Fan,YANG Wenjie,et al. A survey of convolutional neural networks: analysis,applications,and prospects[J].IEEE Transactions on Neural Networks and Learning Systems,2022,33(12):6999-7019.

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结合Prophet-CNN模型的多轨时序InSAR矿区三维形变监测预警

Multi-track sequential InSAR mining 3D deformation monitoring and prediction combined with Prophet-CNN model

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