An InSAR Phase Unwrapping Framework for Large-scale and Complex Events

2026-03-22

AI Summary

This study addresses the challenge of strong phase discontinuities in large-scale InSAR interferograms caused by surface ruptures from shallow earthquakes, which often lead to failure in existing phase unwrapping methods—both traditional algorithms and deep learning models. To tackle this issue, the work proposes an end-to-end unwrapping framework based on diffusion models, incorporating physical constraints to explicitly model physically consistent phase fields. This approach effectively handles interferograms with large spatial extents and high heterogeneity. Experimental results demonstrate that the method exhibits robust performance against phase discontinuities on both synthetic and real-world datasets, significantly outperforming current automated techniques and achieving accuracy comparable to manual unwrapping.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Phase unwrapping remains a critical and challenging problem in InSAR processing, particularly in scenarios involving complex deformation patterns. In earthquake-related deformation, shallow sources can generate surface-breaking faults and abrupt displacement discontinuities, which severely disrupt phase continuity and often cause conventional unwrapping algorithms to fail. Another limitation of existing learning-based unwrapping methods is their reliance on fixed and relatively small input sizes, while real InSAR interferograms are typically large-scale and spatially heterogeneous. This mismatch restricts the applicability of many neural network approaches to real-world data. In this work, we present a phase unwrapping framework based on a diffusion model, developed to process large-scale interferograms and to address phase discontinuities caused by deformation. By leveraging a diffusion model architecture, the proposed method can recover physically consistent unwrapped phase fields even in the presence of fault-related phase jumps. Experimental results on both synthetic and real datasets demonstrate that the method effectively addresses discontinuities associated with near-surface deformation and scales well to large InSAR images, offering a practical alternative to manual unwrapping in challenging scenarios.

Problem

• InSAR
• phase unwrapping
• discontinuities
• large-scale
• deformation

Innovation

• diffusion model
• phase unwrapping
• InSAR
• large-scale processing
• discontinuity handling