AI 中文总结
本文提出一种可扩展贝叶斯框架，结合伴随海平面理论、无限维贝叶斯反演及开源库pygeoinf与pyslfp，通过合成实验及卫星重力与海洋测高数据联合反演验证其可用于现代海平面变化推断。
AI 中文摘要
本文提出了一种用于推断现代海平面变化及地表质量再分布的可扩展贝叶斯框架。为论证该方法的必要性，我们首先回顾并量化了部分分析卫星重力与海洋测高观测数据的标准方法的性能，发现这些方法会大幅低估不确定性，且存在系统性偏差，这些缺陷源于它们对海平面物理过程的处理不完整，以及仅基于观测噪声传播的不确定性估计。为解决这些局限，我们的方法结合了三项改进：其一，利用伴随海平面理论的最新进展，将完整物理过程嵌入正演与反演建模中；其二，在无限维框架下构建贝叶斯反问题，从而避免离散化伪影及截断模型空间固有的不确定性低估问题；其三，我们的计算方法使此类反演能在完整观测分辨率下实现，同时支持联合模型空间与多数据类型。通过采用无矩阵方法，结合迭代求解器与随机低秩分解——在关联开源库pygeoinf和pyslfp中实现——基础计算可在单台笔记本电脑上完成，最密集的任务可在可用核心间轻松并行化。我们通过一系列合成实验验证了该方法，最终完成卫星重力与海洋测高数据的联合反演，将区域平均海平面变化分解为比容分量与气压分量，并给出量化不确定性，包括仍存在的简并性。
英文摘要
This paper presents a scalable Bayesian framework for the inference of modern-day sea-level change and surface mass redistribution. To motivate this approach, we first review and quantify the performance of some standard methods for the analysis of satellite gravity and ocean altimetry observations. We find that these methods substantially underestimate uncertainties and are subject to systematic biases, deficiencies that stem from their incomplete treatment of sea-level physics and from uncertainty estimates based solely on the propagation of observational noise. To address these limitations, our approach combines three advances. First, we use recent developments in adjoint sea-level theory to embed the full physics into the forward and inverse modelling. Second, we formulate the Bayesian inverse problem in an infinite-dimensional setting, thereby avoiding discretisation artefacts and the underestimation of uncertainties inherent in truncated model spaces. Finally, our computational methods render such inversions tractable at full observational resolution while supporting joint model spaces and multiple data types. By employing a matrix-free approach with iterative solvers and randomised low-rank decompositions -- implemented in the linked open-source libraries pygeoinf and pyslfp -- basic calculations are possible on a single laptop, with the most intensive tasks parallelising trivially across available cores. We demonstrate the methodology through a series of synthetic experiments, culminating in a joint inversion of satellite gravity and ocean altimetry data that decomposes regionally averaged sea-level change into steric and manometric components with quantified uncertainties, including the degeneracies that remain.
Comments37 pages; 18 figures; submitted to GJI
