基于ICOPS和ICA方法的时空变形趋势评估,使用工业开发区的多时空InSAR数据
Muhammad Fulki Fadhillah1,2, Wahyu Luqmanul Hakim1,2, Seul-Ki Lee1
1Department of Science Education, Kangwon National University, 1 Gangwondaehak-gil, Chuncheon-si, Gangwon-do, 24341, Republic of Korea.
Scientific reports
|December 23, 2025
概括
合成孔径雷达 (SAR) 监测显示,韩国布戈克工业区的土地沉降幅度很大. 机器学习完善了变形分析,确定了由于工业负载和地下水提取而导致的沉降率高达2.83厘米/年.
科学领域:
- 地质科学和遥感技术
- 地质技术工程 地质技术工程
- 环境监测 环境监测
背景情况:
- 工业扩张需要强大的基础设施和监控.
- 遥感,特别是合成孔径雷达 (SAR),对于分析城市和工业区的表面变形至关重要.
- 之前的研究强调,需要先进的技术来解释复杂的变形模式.
研究的目的:
- 用先进的干扰测量技术评估韩国布戈克工业区的表面变形.
- 为了评估改进的综合散射干扰测量与优化点散射器 (ICOPS) 方法的有效性.
- 确定导致观察到变形的因素及其对基础设施监测和风险管理的影响.
主要方法:
- 采用优化点散射器 (ICOPS) 方法的改进组合散射器干扰度的实施.
- 从2014年到2022年利用Sentinel-1SAR数据 (上升和下降轨道) 和Cosmo SkyMed (CSK) 数据.
- 机器学习算法的应用用于时间序列精制和独立组件分析 (ICA) 进行变形数据分解.
主要成果:
- ICOPS方法成功可视化了线性和非线性时空变形.
- 时间序列分析显示,每年土地沉降幅度在0.76至2.83厘米之间,累计最大变形约为20厘米.
- 变形数据显示季节性,线性季节性和指数趋势,与工业结构和密集的地下水提取有关.
结论:
- 该研究证实,布戈克工业区存在大量沉降,这是由人为因素驱动的.
- 通过机器学习增强的ICOPS方法论,被证明是可靠的分析复杂的变形模式.
- 在SAR技术为工业地区的基础设施建设后监测和风险管理提供了巨大的潜力.
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