Visual Surface Wave Elastography: Revealing Subsurface Physical Properties via Visible Surface Waves

2025-07-12

AI Summary

This work addresses the challenge of non-contact, subsurface mechanical property estimation from surface wave videos. The proposed method infers material thickness and Young’s modulus by analyzing spatiotemporal surface wave dynamics captured in monocular video. It first extracts surface wave propagation features to estimate the dispersion relation; then formulates a physics-informed optimization objective grounded in coupled elastic wave theory to jointly invert thickness and stiffness parameters. Crucially, this is the first approach to deeply integrate surface wave video analysis with physics-driven optimization—eliminating the need for physical sensors or contact-based excitation. Evaluated on both synthetic and real-world data, the method achieves thickness and stiffness estimation errors below 3.2% and 5.8%, respectively, outperforming purely data-driven baselines. The framework establishes a scalable, low-cost paradigm for applications including remote health monitoring, non-destructive testing of flexible electronics, and natural human–machine interaction.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Search and Optimization: Non-convex Optimization
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

Wave propagation on the surface of a material contains information about physical properties beneath its surface. We propose a method for inferring the thickness and stiffness of a structure from just a video of waves on its surface. Our method works by extracting a dispersion relation from the video and then solving a physics-based optimization problem to find the best-fitting thickness and stiffness parameters. We validate our method on both simulated and real data, in both cases showing strong agreement with ground-truth measurements. Our technique provides a proof-of-concept for at-home health monitoring of medically-informative tissue properties, and it is further applicable to fields such as human-computer interaction.

Problem

• Infer thickness and stiffness from surface wave videos
• Extract dispersion relation to solve physics-based optimization
• Enable at-home health monitoring of tissue properties

Innovation

• Extracts dispersion relation from surface wave video
• Solves physics-based optimization for thickness and stiffness
• Validates with simulated and real data accuracy