超大型垃圾填埋场长时序不均匀沉降监测与风险预测

doi:10.11947/j.AGCS.2026.20250489

测绘学报 ›› 2026, Vol. 55 ›› Issue (6): 990-1002.doi: 10.11947/j.AGCS.2026.20250489

• 影像大地测量前沿技术与智慧防灾创新应用 • 上一篇

超大型垃圾填埋场长时序不均匀沉降监测与风险预测

刘骐¹^,²(), 余琛¹^,³^,⁴(), 李振洪¹^,³^,⁴, 宋闯¹^,³^,⁴, 胡晓宁¹^,², 李杰¹^,², 胡腾辉¹^,²

^1.长安大学地质工程与测绘学院，陕西　西安　710054
^2.长安大学地学与卫星大数据研究中心，陕西　西安　710054
^3.长安大学黄土科学全国重点实验室，陕西　西安　710054
^4.长安大学西部矿产资源与地质工程教育部重点实验室，陕西　西安　710054

收稿日期:2025-11-19 修回日期:2026-06-05 发布日期:2026-07-28
通讯作者: 余琛 E-mail:liuqi2002@chd.edu.cn;chen.yu@chd.edu.cn
作者简介:刘骐（2002—），男，博士生，研究方向为洪水监测与InSAR形变监测。E-mail：liuqi2002@chd.edu.cn
基金资助:
国家科技重大专项(2024ZD1000407);国家自然科学基金(42377159)

Long-term uneven subsidence monitoring and risk prediction of ultra-large garbage landfills

Qi LIU¹^,²(), Chen YU¹^,³^,⁴(), Zhenhong LI¹^,³^,⁴, Chuang SONG¹^,³^,⁴, Xiaoning HU¹^,², Jie LI¹^,², Tenghui HU¹^,²

^1.School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
^2.Research Center for Earth Sciences and Satellite Big Data, Chang'an University, Xi'an 710054, China
^3.State Key Laboratory of Loess Science, Chang'an University, Xi'an 710054, China
^4.Key Laboratory of Western Mineral Resources and Geological Engineering of Ministry of Education, Chang'an University, Xi'an 710054, China

Received:2025-11-19 Revised:2026-06-05 Published:2026-07-28
Contact: Chen YU E-mail:liuqi2002@chd.edu.cn;chen.yu@chd.edu.cn
About author:LIU Qi (2002—), male, PhD candidate, majors in flood monitoring and InSAR deformation monitoring. E-mail: liuqi2002@chd.edu.cn
Supported by:
The National Science and Technology Major Project(2024ZD1000407);The National Natural Science Foundation of China(42377159)

摘要/Abstract

摘要：

垃圾填埋场在封场后仍会长期经历由生物降解与压缩引起的持续沉降，其空间差异性强、时序特征复杂，在当前城市固废处置设施向更加安全、长效管理模式转型的背景下，填埋场封场后的地表沉降演化规律变得尤为重要，迫切需要地表形变监测与风险评估。本文以曾为亚洲单体处理规模最大的广东兴丰生活垃圾填埋场为例，整合Envisat和Sentinel-1数据，利用偏移量追踪技术对不同卫星数据处理以补全数据空缺期形变，结合小基线集技术构建了18年的长时间形变序列，并对其未来形变趋势进行了预测。监测结果显示了显著的空间不均匀沉降现象，且不均匀程度与填埋工艺、填埋废物类型高度相关，东部生活垃圾填埋区地面沉降剧烈（2008—2025年间累积沉降4.4 m），西部应急填埋区地表变化相对稳定（2008—2025年间累积沉降1.9 m）。填埋场沉降与气候因子呈现较强相关性，沉降存在3~4个月的时滞效应，揭示了气候变化对填埋场地表稳定性的控制作用。形变预测结果显示未来较长一段时间内，填埋场仍将处于不均匀沉降状态，其中，东部填埋区需8~10年达到地表稳定，西部填埋区则需5年达到地表稳定。本文通过长时序形变监测实现垃圾填埋场全周期形变特征识别，并利用形变分解对各分区的形变特征进行量化；基于ConvLSTM模型，对填埋场未来形变、风险特征及地表稳定时间进行评估，可为类似填埋场的长时序全周期地表形变与风险评估提供参考。

关键词: InSAR, 垃圾填埋场, 形变监测, ConvLSTM模型, 土地安全

Abstract:

Landfills will continue to experience long-term settlement due to biodegradation and compression after closure, with strong spatial variability and complex temporal characteristics. Against the backdrop of the transition of current urban solid waste disposal facilities toward safer and more long-term management models, the evolution of surface settlement after landfill closure has become particularly important, urgently requiring surface deformation monitoring and risk assessment. This paper takes the Guangdong Xingfeng landfill, once the largest single treatment facility in Asia, as an example. It integrates Envisat and Sentinel-1 data, utilizes displacement tracking technology to process different satellite data to fill in deformation gaps during data gaps, and combines the small baseline subset technique to construct an 18-year long-term deformation sequence, while predicting future deformation trends. The monitoring results show significant spatially uneven settlement, with the degree of unevenness highly correlated with landfilling processes and waste types. The eastern landfill area exhibits severe surface settlement (4.4 m of cumulative settlement between 2008 and 2025), while the western emergency landfill area shows relatively stable surface changes (1.9 m of cumulative settlement between 2008 and 2025). The landfill settlement shows a strong correlation with climatic factors, with a 3~4 month lag effect, revealing the control of climate change on the surface stability of landfills. The deformation prediction results indicate that for a long period in the future, the landfill will remain in an uneven settlement state, with the eastern landfill area requiring 8~10 years to achieve surface stability and the western landfill area reaching surface stability in 5 years. This study achieves the identification of the full-cycle deformation characteristics of landfills through long-term deformation monitoring and quantifies the deformation characteristics of each division using deformation decomposition. Based on the ConvLSTM model, it evaluates the future deformation, risk characteristics, and surface stability time of the landfill. The research approach can provide a reference for the long-term full-cycle surface deformation and risk assessment of similar landfills.

Key words: InSAR, garbage landfills, deformation monitoring, ConvLSTM model, land safety

中图分类号:

P228

刘骐, 余琛, 李振洪, 宋闯, 胡晓宁, 李杰, 胡腾辉. 超大型垃圾填埋场长时序不均匀沉降监测与风险预测[J]. 测绘学报, 2026, 55(6): 990-1002.

Qi LIU, Chen YU, Zhenhong LI, Chuang SONG, Xiaoning HU, Jie LI, Tenghui HU. Long-term uneven subsidence monitoring and risk prediction of ultra-large garbage landfills[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(6): 990-1002.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20250489

http://xb.chinasmp.com/CN/Y2026/V55/I6/990

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卫星	轨道	极化方式	成像模式	开始日期	截止日期	影像数量
Envisat	升轨	VV	IM	2008-01-07	2010-09-26	33
Sentinel-1	升轨	VV	IW	2015-06-15	2025-05-17	273

点名	P11	P12	P13	P14	P15	P16	P17	P21	P22	P23
标准差	99.28	140.33	111.12	153.59	204.63	175.86	104.28	56.86	38.24	21.70
标准差（校正）	36.71	76.24	81.95	114.64	170.27	151.39	84.91	55.96	33.25	17.35

特征点	温度相关性（Pearson系数）	温度相关性（Spearman系数）	降水相关性（Pearson系数）	降水相关性（Spearman系数）	温度时滞/d	降水时滞/d
P11	0.72	0.68	0.69	0.64	115	108
P12	0.75	0.81	0.74	0.82	124	104
P13	0.76	0.63	0.79	0.58	105	106
P14	0.69	0.74	0.64	0.69	111	118
P15	0.66	0.59	0.69	0.54	115	124
P16	0.59	0.71	0.61	0.67	118	132
P17	0.62	0.56	0.57	0.52	101	99
P21	0.58	0.66	0.59	0.73	132	114
P22	0.64	0.78	0.54	0.76	119	110
P23	0.53	0.61	0.51	0.49	119	124

模型	RMSE/mm	R²
ConvLSTM	21.2	0.892
LSTM	25.8	0.831
GRU	26.4	0.816
Transformer	28.2	0.789
ARIMA	32.5	0.724

超大型垃圾填埋场长时序不均匀沉降监测与风险预测

Long-term uneven subsidence monitoring and risk prediction of ultra-large garbage landfills

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