利用预训练速度模型生成式Transformer网络将先验从浅层传播到深层
Propagating the prior from shallow to deep with a pre-trained velocity-model Generative Transformer network
AI总结：
针对现有速度模型生成器忽略空间依赖和分辨率变化的问题，提出VelocityGPT，采用自回归训练的Transformer解码器自上而下生成模型，结合浅层先验与额外压缩网络，经合成数据验证可有效用于地震速度建模。
摘要：
构建地下速度模型对于我们利用地震数据进行地球探测、勘探以及监测的目标至关重要。随着机器学习的兴起，这些速度模型（或更准确地说，它们的分布）可以在生成模型中被准确高效地存储。这些存储的速度模型分布可用于对全波形反演等反演问题进行正则化或量化不确定性。然而，大多数生成器（如归一化流或扩散模型）对图像（速度模型）进行统一处理，忽略了空间依赖性以及相对于观测位置的分辨率变化。为解决这一缺陷，我们提出VelocityGPT，这是一种新颖的实现方案，利用以自回归方式训练的Transformer解码器，从地下浅层到深层生成速度模型。由于地震数据通常在地球表面记录，自上而下的生成器可以利用浅层的反演信息作为引导（先验）来生成深层模型。为便于实现，我们使用一个额外的网络来压缩速度模型。我们还注入先验信息，如测井数据或（由偏移图像表示的）构造信息，以生成速度模型。通过合成数据，我们证明了VelocityGPT作为一种有前景的方法，在地震速度建模的生成模型应用中具有有效性。
英文摘要：
Building subsurface velocity models is essential to our goals in utilizing seismic data for Earth discovery and exploration, as well as monitoring. With the dawn of machine learning, these velocity models (or, more precisely, their distribution) can be stored accurately and efficiently in a generative model. These stored velocity model distributions can be utilized to regularize or quantify uncertainties in inverse problems, like full waveform inversion. However, most generators, like normalizing flows or diffusion models, treat the image (velocity model) uniformly, disregarding spatial dependencies and resolution changes with respect to the observation locations. To address this weakness, we introduce VelocityGPT, a novel implementation that utilizes Transformer decoders trained autoregressively to generate a velocity model from shallow subsurface to deep. Owing to the fact that seismic data are often recorded on the Earth's surface, a top-down generator can utilize the inverted information in the shallow as guidance (prior) to generating the deep. To facilitate the implementation, we use an additional network to compress the velocity model. We also inject prior information, like well or structure (represented by a migration image) to generate the velocity model. Using synthetic data, we demonstrate the effectiveness of VelocityGPT as a promising approach in generative model applications for seismic velocity model building.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
