发表机构
Bureau of Economic Geology, Jackson School of Geosciences The University of Texas at Austin（德克萨斯大学奥斯汀分校杰克逊地球科学学院经济地质局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传统频散分析计算代价高且深度学习忽略反问题非唯一性的问题，提出物理引导多模态变分自编码器（PG-VAE），融合炮集与频散图像并引入物理一致性正则化，实现可扩展近地表表征，并在野外数据上验证有效。
AI 中文摘要
地震采集技术的进步带来了大规模勘探，使得传统的手动频散分析在逻辑和计算上变得不可行。虽然深度学习技术可以提供自动化替代方案，但标准卷积神经网络（CNNs）往往无法考虑反问题的非唯一性，并可能产生地质上不切实际的平滑速度模型。为应对这些挑战，我们提出了一种物理引导的多模态变分自编码器（PG-VAE），用于可扩展的近地表表征。我们的框架引入了一种多流融合策略，利用瑞利波频散的物理原理，从原始炮集和频谱-频散图像中映射独立的潜在特征。我们实现了一种专门的解码器架构，采用学习型转置卷积和残差细化，以强制对层状地球模型的结构性偏向。通过一个可微分的代理网络引入物理引导，该网络作为前向一致性正则化器，惩罚违反瑞利波频散物理规律的预测。该网络在通过弹性建模生成的多样化合成数据上训练，然后在未见过的合成数据上验证，随后在德克萨斯州Devine试验场的超高密度野外数据集上评估。我们还采用梯度加权类激活映射（Grad-CAM）来检查对网络预测有贡献的输入特征。所提出的方法可作为近地表表征的可扩展工具，以及高分辨率弹性全波形反演的初始模型构建工具。
英文摘要
The growth in seismic acquisition techniques has led to large-scale surveys, making traditional manual dispersion analysis logistically and computationally prohibitive. While deep learning techniques can offer an automated alternative, standard convolutional neural networks (CNNs) often fail to account for the non-uniqueness of the inverse problem and can produce geologically unrealistic, smooth velocity models. To address these challenges, we propose a physics-guided multi-modal variational autoencoder (PG-VAE) for scalable near-surface characterization. Our framework introduces a multi-stream fusion strategy that maps independent latent features from both raw shot gathers and spectral-dispersion images by leveraging the physics of Rayleigh-wave dispersion. We implement a specialized decoder architecture with learned transposed convolutions and residual refinement to enforce a structural bias towards layered earth models. Physics guidance is incorporated via a differentiable surrogate network that serves as a forward-consistency regularizer, penalizing predictions that violate the physics of Rayleigh-wave dispersion. The network is trained on diverse synthetic data generated via elastic modeling, and then validated on unseen synthetic data, and subsequently evaluated on an ultra-high-density field dataset from the Devine test site in Texas. We also employ gradient-weighted class activation mapping (Grad-CAM) to examine the input features contributing to the network predictions. The proposed method could serve as a scalable tool for near-surface characterization and as an initial model-building tool for high-resolution elastic full-waveform inversion.
