用神经网络将地震速度模型压缩19倍，还能无损放大到更高分辨率。
High-Fidelity Compression of Seismic Velocity Models via SIREN Auto-Decoders
- 用SIREN自编码器将70x70速度图压缩为256维向量。
- 重建平均保持32.47 dB的保真度和0.956的结构相似性。
- 支持插值生成新地质结构，且无需训练即可超分到280x280。
隐式神经表示（INRs）作为一种与网格分辨率无关的连续信号表示方法，展现出强大潜力。本文提出一种基于SIREN自编码器的高保真神经压缩框架，用于表示来自OpenFWI基准的多结构地震速度模型。该方法将每个70×70的速度图（共4,900个点）压缩为256维的紧凑潜在向量，实现19:1的压缩比。在1,000个样本、五类不同地质结构（FlatVel、CurveVel、FlatFault、CurveFault、Style）上的实验表明，平均峰值信噪比达32.47 dB，结构相似性为0.956，重建质量优异。此外，我们展示了隐式表示的两大优势：（1）潜在空间平滑插值可生成合理的中间速度结构；（2）零样本超分辨率能力，可在不额外训练的情况下将速度场重建至任意分辨率，最高达280×280。结果表明，基于INR的自编码器在高效存储、多尺度分析及全波形反演等地球物理应用中具有巨大潜力。
原文摘要 · Abstract (English)
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 of 32.47 dB and SSIM of 0.956, indicating high-quality reconstruction. Furthermore, we showcase two key advantages of our implicit representation: (1) smooth latent space interpolation that generates plausible intermediate velocity structures, and (2) zero-shot super-resolution capability that reconstructs velocity fields at arbitrary resolutions up to 280x280 without additional training. The results highlight the potential of INR-based auto-decoders for efficient storage, multi-scale analysis, and downstream geophysical applications such as full waveform inversion.
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