AI RESEARCH
High-Fidelity Compression of Seismic Velocity Models via SIREN Auto-Decoders
arXiv CS.AI
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ArXi:2603.14284v1 Announce Type: cross Implicit Neural Representations (INRs) have emerged as a powerful paradigm for representing continuous signals independently of grid resolution. In this paper, we propose a high-fidelity neural compression framework based on a SIREN (Sinusoidal Representation Networks) auto-decoder to represent multi-structural seismic velocity models from the OpenFWI benchmark. Our method compresses each 70x70 velocity map (4,900 points) into a compact 256-dimensional latent vector, achieving a compression ratio of 19:1.