时序InSAR滑坡形变监测与预测的N-BEATS深度学习法——以新铺滑坡为例
滑坡通常发生突然, 破坏力巨大, 经常造成重大生命安全事故和财产损失。高可靠性、高精度及具有抗差性能的滑坡形变监测预测手段和方法对于国家防灾减灾需求具有切实意义。InSAR技术是一种能够全天时和全天候观测获取高空间分辨率和宽覆盖率影像, 高灵敏性捕捉时空维动态变化的监测手段, 然而目前应用InSAR时序影像对滑坡区进行滑坡预测的工作仅是凤毛麟角。基于时序InSAR观测结果, 本文提出了一种能够有效解决中短期滑坡预测问题的深度学习滑坡预测方法。在三峡新铺滑坡区应用N-BEATS网络模型和Sentinel-1 SAR数据进行形变预测, 以均方根误差1.1mm的预测精度完成了滑坡预测工作, 并对预测...
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| Published in | Ce hui xue bao Vol. 51; no. 10; pp. 2171 - 2182 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | Chinese English |
| Published |
Beijing
Surveying and Mapping Press
01.10.2022
中南大学地球科学与信息物理学院,湖南长沙410083%长沙天仪空间科技研究院有限公司,湖南长沙410205%长安大学地质工程与测绘学院,陕西西安710054 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-1595 1001-1595 |
| DOI | 10.11947/j.AGCS.2022.20220298 |
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| Abstract | 滑坡通常发生突然, 破坏力巨大, 经常造成重大生命安全事故和财产损失。高可靠性、高精度及具有抗差性能的滑坡形变监测预测手段和方法对于国家防灾减灾需求具有切实意义。InSAR技术是一种能够全天时和全天候观测获取高空间分辨率和宽覆盖率影像, 高灵敏性捕捉时空维动态变化的监测手段, 然而目前应用InSAR时序影像对滑坡区进行滑坡预测的工作仅是凤毛麟角。基于时序InSAR观测结果, 本文提出了一种能够有效解决中短期滑坡预测问题的深度学习滑坡预测方法。在三峡新铺滑坡区应用N-BEATS网络模型和Sentinel-1 SAR数据进行形变预测, 以均方根误差1.1mm的预测精度完成了滑坡预测工作, 并对预测结果进行了数据结构影响的规律性分析、传统方法效果对比、抗差性评估及置信区间估计等多方位的剖析, 结果显示出了其高精度、高可靠性及具有一定抗差能力的突出优势。 |
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| AbstractList | P237; 滑坡通常发生突然,破坏力巨大,经常造成重大生命安全事故和财产损失.高可靠性、高精度及具有抗差性能的滑坡形变监测预测手段和方法对于国家防灾减灾需求具有切实意义.InSAR技术是一种能够全天时和全天候观测获取高空间分辨率和宽覆盖率影像,高灵敏性捕捉时空维动态变化的监测手段,然而目前应用InSAR时序影像对滑坡区进行滑坡预测的工作仅是凤毛麟角.基于时序InSAR观测结果,本文提出了一种能够有效解决中短期滑坡预测问题的深度学习滑坡预测方法.在三峡新铺滑坡区应用N-BEATS网络模型和Sentinel-1 SAR数据进行形变预测,以均方根误差1.1mm的预测精度完成了滑坡预测工作,并对预测结果进行了数据结构影响的规律性分析、传统方法效果对比、抗差性评估及置信区间估计等多方位的剖析,结果显示出了其高精度、高可靠性及具有一定抗差能力的突出优势. 滑坡通常发生突然, 破坏力巨大, 经常造成重大生命安全事故和财产损失。高可靠性、高精度及具有抗差性能的滑坡形变监测预测手段和方法对于国家防灾减灾需求具有切实意义。InSAR技术是一种能够全天时和全天候观测获取高空间分辨率和宽覆盖率影像, 高灵敏性捕捉时空维动态变化的监测手段, 然而目前应用InSAR时序影像对滑坡区进行滑坡预测的工作仅是凤毛麟角。基于时序InSAR观测结果, 本文提出了一种能够有效解决中短期滑坡预测问题的深度学习滑坡预测方法。在三峡新铺滑坡区应用N-BEATS网络模型和Sentinel-1 SAR数据进行形变预测, 以均方根误差1.1mm的预测精度完成了滑坡预测工作, 并对预测结果进行了数据结构影响的规律性分析、传统方法效果对比、抗差性评估及置信区间估计等多方位的剖析, 结果显示出了其高精度、高可靠性及具有一定抗差能力的突出优势。 |
| Author | 胡俊 朱武 杜志贵 桂容 郭澳庆 郑万基 贺乐和 |
| AuthorAffiliation | 中南大学地球科学与信息物理学院,湖南长沙410083%长沙天仪空间科技研究院有限公司,湖南长沙410205%长安大学地质工程与测绘学院,陕西西安710054 |
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| Author_FL | ZHU Wu DU Zhigui GUO Aoqing HU Jun ZHENG Wanji GUI Rong HE Lehe |
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| Copyright | Oct 2022. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Snippet | 滑坡通常发生突然, 破坏力巨大, 经常造成重大生命安全事故和财产损失。高可靠性、高精度及具有抗差性能的滑坡形变监测预测手段和方法对于国家防灾减灾需求具有切实意... P237; 滑坡通常发生突然,破坏力巨大,经常造成重大生命安全事故和财产损失.高可靠性、高精度及具有抗差性能的滑坡形变监测预测手段和方法对于国家防灾减灾需求具有切实意... |
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| SubjectTerms | Canyons Data structures Deep learning Emergency preparedness Interferometric synthetic aperture radar Landslides Monitoring Monitoring methods Predictions Reliability Spatial discrimination Spatial resolution |
| Title | 时序InSAR滑坡形变监测与预测的N-BEATS深度学习法——以新铺滑坡为例 |
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