基于数据同化理论的InSAR技术黄河上游滑坡监测预报

刘文涛; 雷浩川; 马顺

doi:10.13686/j.cnki.dzyzy.2024.02.012

基于数据同化理论的InSAR技术黄河上游滑坡监测预报

青海大学地质工程系, 青海西宁 810016

基金项目:
国家自然科学基金项目"湟水河流域典型农田面源污染生态环境影响与系统防控机制研究"(U20A20115);青海大学"创新创业工坊"项目(GF-20230005)

详细信息

作者简介: 刘文涛(1998-), 男, 硕士研究生, 测绘工程专业, 主要从事地质灾害监测研究, 通信地址青海省西宁市城北区宁大路251号, E-maill: //1142282774@qq.com

通讯作者: 雷浩川(1973-)男, 博士, 讲师, 主要从事遥感与地理信息教学与研究, 通信地址青海省西宁市城北区宁大路251号, E-maill//56242118@qq.com

中图分类号: P642.2

收稿日期: 2023-04-17

修回日期: 2023-07-03

刊出日期: 2024-04-25

LANDSLIDE MONITORING AND FORECASTING IN UPPER YELLOW RIVER BY InSAR TECHNOLOGY BASED ON DATA ASSIMILATION THEORY

Department of Geological Engineering, Qinghai University, Xining 810016, China

More Information

Corresponding author: LEI Hao-chuan, 56242118@qq.com

Received Date: 17 April 2023

Revised Date: 03 July 2023

Publish Date: 25 April 2024

摘要

摘要:
黄河上游地区滑坡地质灾害分布广泛, 活动频繁, 危害严重. 传统的滑坡识别和监测方法均存在局限性. InSAR技术由于其精度高、可获取毫米级形变等特点被广泛应用于滑坡监测中. 但InSAR技术对影像相干性的要求较高, 导致其数据的离散程度较大, 无法获取到连续的形变数据, 对实际应用中利用监测的结果进行预报的能力造成了较大的影响. 而利用数据同化理论的建模方法, 可以对多尺度、多来源、多类型的数据进行协同处理以消除误差. 本文通过SBAS-InSAR技术获取地表的毫米级形变, 经由卡尔曼滤波算法对观测结果进行了数据同化. 单点实验的结果表明, 卡尔曼滤波之后的模拟结果较同化之前有了明显的提高, 验证了数据同化算法在提高数值模拟的精度上的可行性. 通过数据同化理论来产生连续的预报数据, 为InSAR技术监测和预报滑坡形变提供了一个新的思路.
- 光学遥感 /
- InSAR /
- 监测预报 /
- 数据同化 /
- 滑坡 /
- 地质灾害
Abstract:
The landslides in the Upper Yellow River region are frequent and widely distributed, causing serious damage. Traditional landslide recognition and monitoring methods have limitation, while the InSAR technology, due to its high precision and extraction of millimeter-scale deformation, is widely used in landslide monitoring. However, the technique has a high requirement for image coherence, resulting in large dispersion degree of data and impossibility of obtaining continuous deformation data, which greatly impacts the forecasting application of the monitoring results. With the modeling method of data assimilation theory, the multiscale, multisource and multitype data can be coprocessed to eliminate errors. In this paper, the millimeter-scale deformation of surface is obtained by SBAS-InSAR technology, and the observed data is assimilated by Kalman filtering(KF) algorithm. The single point experiment shows that the simulation results after KF are significantly improved compared with those before the assimilation, which verifies the feasibility of data assimilation algorithm in improving the precision of numerical simulation. The continuous forecast data is generated by data assimilation theory, which provides a new way for InSAR technology to monitor and forecast landslide deformation.
- optical remote sensing /
- InSAR /
- monitoring and forecasting /
- data assimilation /
- landslide /
- geological disaster

HTML全文

图 1 研究区域范围

Figure 1.

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图 2 SAR降轨数据覆盖范围

Figure 2.

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图 3 InSAR提取的形变区范围及严重形变点分布图

Figure 3.

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图 4 可见光影像识别的滑坡

Figure 4.

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图 5 卡尔曼滤波的预报结果

Figure 5.

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图 6 均方根误差与平均绝对误差统计

Figure 6.

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表 1 沉降点模型预报及卡尔曼滤波结果误差统计

Table 1. Error statistics of subsidence point model forecast and KF results

点号	1	2	3	4	5	6
模型RMES	0.00563	0.00516	0.00523	0.00498	0.00485	0.004190
卡尔曼滤波RMES	0.00150	0.00112	0.00137	0.00113	0.00113	0.001060
模型MAE	-0.00134	-0.00087	-0.00080	-0.00042	0.00078	-0.000003
卡尔曼滤波MAE	-0.00035	-0.00019	-0.00021	-0.00010	0.00018	-0.000001

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表 2 摆动点、抬升点模型预报及卡尔曼滤波结果误差统计

Table 2. Error statistics of oscillating and lifting point model forecast and KF results

点号	7	8	9	10
模型RMES	0.00524	0.00422	0.00346	0.00495
卡尔曼滤波RMES	0.00125	0.00100	0.00088	0.00107
模型MAE	-0.00026	-0.00128	-0.00025	0.00011
卡尔曼滤波MAE	-0.00006	-0.00030	-0.00006	0.00002

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