High-Fidelity Compression of Seismic Velocity Models via SIREN Auto-Decoders
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. We evaluate the framework on 1,000 samples across five diverse geological families: FlatVel, CurveVel, FlatFault, CurveFault, and Style. Experimental results demonstrate an average PSNR ...
| Authors | Caiyun Liu, Xiaoxue Luo, Jie Xiong |
|---|---|
| Primary category | cs.LG |
| Published | 15 March 2026 |
| Last updated at source | 17 March 2026 |
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