标题： 通过预训练速度模型的生成变换器网络从浅层到深层传播先验 显示英文标题

标题： Propagating the prior from shallow to deep with a pre-trained velocity-model Generative Transformer network

摘要： 构建地下速度模型对于利用地震数据进行地球探索和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）可以被准确且高效地存储在生成模型中。这些存储的速度模型分布可以用于正则化或量化反问题中的不确定性，例如全波形反演。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，忽略了与观测位置相关的空间依赖性和分辨率变化。为了解决这一弱点，我们引入了VelocityGPT，这是一种新颖的实现方法，利用自回归训练的Transformer解码器从浅层地下生成深层速度模型。由于地震数据通常是在地球表面记录的，一个自上而下的生成器可以利用浅层的反演信息作为指导（先验）来生成深层。为了便于实现，我们使用了一个额外的网络来压缩速度模型。我们还注入了先验信息，如井数据或结构（由迁移图像表示）以生成速度模型。使用合成数据，我们展示了VelocityGPT在地震速度模型构建的生成模型应用中的有效性，作为一种有前景的方法。 显示英文摘要

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.