Three-dimensional (3-D) deformation could be resolved using multitrack Interferometric Synthetic Aperture Radar (InSAR), with the accuracy dependent on the magnitude of multisource errors within InSAR measurements. To improve the precision of 3-D deformation, it is essential to understand the error propagation mechanism and then develop the methodology for reducing error impacts in 3-D decomposition processing. In this article, we present an error propagation model that incorporates both systematic and stochastic error propagation, which determines the error contribution of the multitrack InSAR measurements in the 3-D direction. The systematic error propagation includes generic systematic error and additional systematic errors (ASEs) in the vertical and east directions caused by neglecting the north component. For stochastic error propagation, we construct the covariance matrix by considering variance and correlation from different InSAR measurements when using differential and multitemporal InSAR (MT-InSAR) techniques. Accordingly, we propose a new 3-D deformation inversion method, combining the covariance matrix and L^2-norm regularization based on multitrack InSAR (CovRM-InSAR) to improve the precision of 3-D deformation with noise reduction. In the case study, we applied Sentinel-1A and ALOS-2 InSAR datasets from four tracks to map 3-D velocity in Wuhai and analyzed the time-series error propagation and 3-D uncertainty. The precision of 3-D deformation resolved by CovRM-InSAR has improved by up to 90%, 44%, and 98% in the vertical, east, and north directions, respectively. Additionally, the CovRM-InSAR has effectively reduced the stochastic errors by up to 38%, 15%, and 90% in the vertical, east, and north directions, respectively.

中文翻译：

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多轨 InSAR 观测到 3-D 表面形变估计的误差传播和误差缓解

三维 (3-D) 变形可以使用多轨干涉合成孔径雷达 (InSAR) 来解决，其精度取决于 InSAR 测量中多源误差的大小。为了提高 3D 变形的精度，有必要了解误差传播机制，然后开发减少 3D 分解处理中误差影响的方法。在本文中，我们提出了一种误差传播模型，该模型结合了系统误差传播和随机误差传播，该模型确定了多轨 InSAR 测量在 3D 方向上的误差贡献。系统误差传播包括一般系统误差和由于忽略北向分量而引起的垂直和东向的附加系统误差（ASE）。对于随机误差传播，我们在使用差分和多时相 InSAR (MT-InSAR) 技术时，通过考虑不同 InSAR 测量的方差和相关性来构建协方差矩阵。因此，我们提出了一种新的3D变形反演方法，结合协方差矩阵和基于多轨InSAR（CovRM-InSAR）的L^2范数正则化，以提高3D变形和降噪的精度。在案例研究中，我们应用来自四个轨道的Sentinel-1A和ALOS-2 InSAR数据集来绘制乌海的3D速度图，并分析时间序列误差传播和3D不确定性。 CovRM-InSAR 解析的 3 维形变精度在垂直、东向和北向分别提高了 90%、44% 和 98%。此外，CovRM-InSAR还有效地将垂直、东、北方向的随机误差分别降低了38%、15%和90%。