Remote sensing characterizing and deformation predicting of Yan'an New District’s Mountain Excavation and City Construction with dual-polarization MT-InSAR method

IF 7.5 1区 地球科学 Q1 Earth and Planetary Sciences International Journal of Applied Earth Observation and Geoinformation Pub Date : 2025-01-15 DOI:10.1016/j.jag.2025.104364
Yanan Jiang, Qiang Xu, Ran Meng, Chao Zhang, Linfeng Zheng, Zhong Lu
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Abstract

The Mountain Excavation and City Construction project (MECC) in Yan’an New District (YND) on the Chinese Loess Plateau is one of the largest geotechnical works globally. Ground deformation resulting from these extensive earthworks continues to evolve spatially and temporally even after construction is completed. Monitoring this deformation is crucial for understanding uneven post-construction subsidence and ensuring the structural integrity of infrastructure. This study proposes a framework for monitoring and predicting post-construction ground settlement (PCGS) using a dual-polarization Multi-temporal InSAR method (dual-pol MT-InSAR) and Self-Attention Memory Convolutional Long Short-Term Memory (SAM-ConvLSTM) model. Compared to single-polarization (single-pol) MT-InSAR methods, the dual-pol MT-InSAR approach, which utilizes both polarization channels of Sentinel-1 (S1) SAR data, achieves a 24 % increase in Permanent Scatterer (PS) density for PS-InSAR and improves average coherence while reducing coherence standard deviation for Small Baseline Subset (SBAS). The study further examines the factors contributing to uneven ground deformation, including fill and excavation activities (e.g., the thickness and geotechnical properties of loess), construction activities and surface loads, and precipitation. A consolidation settlement model is employed to simulate and assess ground settlement decay due to loess compaction. Based on this analysis, the most affected area in Qiaoergou is selected for spatiotemporal forecasting using MT-InSAR measurements and the SAM-ConvLSTM model. The results indicate that regions with significant subsidence form a characteristic funnel shape, with subsidence increasing over time and the deformation perimeter expanding outward. The model achieved an average absolute error of 1.6 mm, with the majority of errors concentrated within 5 mm.
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基于双极化MT-InSAR方法的延安新区山地开挖与城市建设遥感特征与变形预测
中国黄土高原延安新区山地开挖与城市建设工程(MECC)是世界上最大的岩土工程之一。这些大规模的土方工程造成的地面变形即使在施工完成后仍在空间和时间上继续演变。监测这种变形对于了解施工后不均匀沉降和确保基础设施的结构完整性至关重要。本研究提出了一种基于双极化多时相InSAR方法(dual-pol MT-InSAR)和自注意记忆卷积长短期记忆(SAM-ConvLSTM)模型的施工后地面沉降监测与预测框架。与单极化(单极化)MT-InSAR方法相比,双极化MT-InSAR方法利用了Sentinel-1 (S1) SAR数据的两个极化通道,使PS- insar的永久散射体(PS)密度增加了24%,提高了平均相干性,同时降低了小基线子集(SBAS)的相干标准偏差。该研究进一步探讨了导致地面不均匀变形的因素,包括填土和开挖活动(如黄土的厚度和岩土力学性质)、施工活动和地表荷载以及降水。采用固结沉降模型对黄土压实作用下的地基沉降衰减进行了模拟和评价。在此基础上,利用MT-InSAR测量数据和SAM-ConvLSTM模型,选取桥耳沟受影响最严重的区域进行时空预报。结果表明:沉降显著区域呈典型的漏斗状,沉降随时间增大,变形周长向外扩展;模型的平均绝对误差为1.6 mm,大部分误差集中在5 mm以内。
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来源期刊
CiteScore
10.20
自引率
8.00%
发文量
49
审稿时长
7.2 months
期刊介绍: The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.
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