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拉皮德眼子像素相关的错误纠正:2019年里奇克雷斯特地震序列的案例研究
Wulinhong Luo1, Qi An1, Guangcai Feng1
1School of Geosciences and Info-Physics, Central South University, Changsha 410083, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了使用RapidEye卫星图像分析表面变形的改进方法. 新技术显著提高了变形测量的准确性,这对于监测地质事件至关重要.
科学领域:
- 地质科学 地质科学
- 遥感 遥感 遥感 遥感
- 地质物理学 地质物理学
背景情况:
- 光学图像子像素相关性 (SPC) 对于监测大规模表面变形至关重要.
- 快速眼镜图像提供高空间分辨率和短的复查时间,非常适合用于变形监测.
- 现有的研究缺乏对RapidEye变形数据的错误来源和纠正方法的全面分析.
研究的目的:
- 为了全面分析RapidEye偏移字段中的错误来源.
- 开发和验证RapidEye衍生变形场的有效错误校正方法.
- 使用RapidEye图像来提高大规模表面变形监测的精度.
主要方法:
- 在使用无变形图像的RapidEye偏移场中分析了错误来源 (脱相关噪声,轨道错误,态度动).
- 应用空间过和仔细的偏移对选择,以减轻脱关系噪声.
- 利用多项式适配用于轨道错误校正和线性适配与态度动连贯性用于动扭曲减轻.
- 通过提取2019年里奇克雷斯特地震序列的共震位移场来验证方法.
主要成果:
- 在RapidEye偏移场中,确定了折叠关系噪声,轨道误差和态度动作为主要错误.
- 通过拟议的空间过,多项式拟合和线性拟合方法,证明了有效地减轻这些错误.
- 提取了2019年里奇克雷斯特地震序列的2D偏移场,显示最大位移2.8米 (E-W) 和2.4米 (N-S).
- 取得了显著的精度改进:与错误纠正后的GNSS观测相比,在E-W方向达到92%和N-S方向达到89%.
结论:
- 提出的错误纠正方法显著提高了使用RapidEye数据监测表面变形的准确性.
- 这些经过验证的技术对于分析地震移位和其他大规模变形事件是有效的.
- 这项研究为利用RapidEye图像在精确地质危险监测中提供了强大的框架.
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