Unveiling the Mechanism of Continuous Representation Full-Waveform Inversion: A Wave Based Neural Tangent Kernel Framework

1. 1Represent parameter models with coordinate-based neural networks
2. 2Apply neural tangent kernel framework to improve sensitivity to initial models
3. 3Use wave-based formulation to incorporate physical constraints
4. 4Evaluate the proposed framework on geophysical, medical, and non-destructive testing applications

This research benefits geophysicists, medical imaging professionals, and non-destructive testing experts who rely on full-waveform inversion methods, as well as AI engineers and researchers working on neural networks and inverse problems

💡 The proposed framework improves the sensitivity of full-waveform inversion to initial models using a wave-based neural tangent kernel approach

🌊 Unveiling the mechanism of continuous representation full-waveform inversion with wave-based neural tangent kernel framework! 🤯