With the rapid advancements in synthetic aperture radar (SAR) satellites and associated processing algorithms over recent decades, interferometric SAR (InSAR) has emerged as a routine method for monitoring large-scale ground deformation and interpreting geophysical processes. Statistical inference serves as a major component in InSAR technique developments and applications. This article provides an overview of InSAR deformation measurement and InSAR-constrained geophysical inversion, using a statistical inference point of view. Its objectives are to facilitate understanding of the method by addressing its underlying mathematical challenges. We begin by introducing the concept of statistical inference and the structure of our content organization framework. Next, we investigate the distinct concerns associated with statistical inference in InSAR deformation measurement and InSAR-constrained geophysical inversion. Finally, we propose several significant directions for future research. Table 1 includes abbreviations used throughout this article. Additionally, we highlight relevant resources, such as mathematical background, open source codes, and data repositories, in an appendix, which is available as supplementary material at https://doi.org/10.1109/MGRS.2023.3344159 .

中文翻译：

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干涉合成孔径雷达变形测量和地球物理反演中的统计推断：综述


近几十年来，随着合成孔径雷达（SAR）卫星和相关处理算法的快速发展，干涉SAR（InSAR）已成为监测大规模地面变形和解释地球物理过程的常规方法。统计推断是InSAR技术开发和应用的主要组成部分。本文从统计推断的角度概述了 InSAR 变形测量和 InSAR 约束地球物理反演。其目标是通过解决其潜在的数学挑战来促进对该方法的理解。我们首先介绍统计推断的概念和内容组织框架的结构。接下来，我们研究与 InSAR 变形测量和 InSAR 约束地球物理反演中的统计推断相关的独特问题。最后，我们提出了未来研究的几个重要方向。表 1 包括本文中使用的缩写。此外，我们在附录中重点介绍了相关资源，例如数学背景、开源代码和数据存储库，该附录可作为补充材料在 https://doi.org/10.1109/MGRS.2023.3344159 上获取。