Explainable and Uncertainty-Aware Computational Geodetic Inversion: A Review and Framework for Multi-Source Earth Observation

Geodetic inversion converts surface and remote sensing data into estimates of concealed Earth system parameters, including fault slip, magma-source geometry, subsurface mass changes, crustal strain, and time-dependent geohazard activities. This review explores geodetic inversion as a computational challenge that involves combining diverse data, optimizing and quantifying uncertainty, and providing explainable decision support. A narrative technical review was compiled using literature records from Scopus and Dimensions on topics such as geodetic inversion, Global Navigation Satellite System (GNSS) measurements, Interferometric Synthetic Aperture Radar (InSAR), gravity and seismic source modeling, Bayesian inversion, sparse regularization, and physics-informed learning. The literature was sorted based on the data types, inverse-problem structures, algorithmic groups, uncertainty management, and reporting standards. This review highlights five common computational layers in contemporary geodetic inversion: data harmonization, forward-model encoding, inversion and optimization, uncertainty propagation, and explainable decision support. These layers are synthesized into an explainable artificial intelligence-uncertainty quantification-computational geodetic inversion (XAI-UQ-CGI) framework for multi-source Earth observation. To show that the framework is computationally executable rather than only conceptual, it is demonstrated on a controlled synthetic same-model experiment in which a Mogi volcanic point-pressure source is inverted from synthetic InSAR and GNSS data, yielding empirically assessed least-squares uncertainty intervals and a quantifiable reduction of depth non-uniqueness under sensor fusion. Future geodetic inversion studies should move beyond single best-fit solutions toward reproducible, physics-constrained, uncertainty-preserving, and human-interpretable computational workflows suitable for geohazard monitoring and Digital Earth applications.