发表机构
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传统速度建模依赖高质量初始模型且忽略浅深层递进关系的问题，提出深度递进扩散框架，从浅到深增量构建速度模型，利用成对块训练和混合融合消除伪影，实验验证高精度及不确定性量化能力。
AI 中文摘要
地震速度模型构建（VMB）是理解地下结构的基础。传统方法需要高质量的初始模型，且在覆盖范围上分辨率有限，计算强度大。近期基于生成扩散模型的方法通过捕获统计先验来支持传统反演方法，但这些方法未考虑地表记录数据中信息自上而下的递进（层剥离）过程，其中深层速度信息依赖于浅层。为解决此问题，我们提出了一种深度递进扩散框架，通过传播先验信息，从浅到深逐步构建速度模型。我们的方法在具有可变重叠和显式深度编码的成对浅-深速度块上进行训练，并整合了包括测井数据和地震图像（代表结构信息）在内的多种地球物理约束。在推理过程中，我们使用递进算法合成重叠的深度切片，并通过高斯加权混合进行融合，以消除边界伪影。该方法利用了学习到的地质分布和观测到的浅层先验，同时提供了不确定性量化。在分布内测试和分布外测试上的大量数值实验表明，VMB精度优异，预测不确定性与实际误差之间存在强相关性。作为概念验证，本第一部分采用源自垂直反射率的理想化结构约束来验证方法框架。配套论文（第二部分）将该方法扩展到依赖偏移图像的实际结构约束，并应用于现场数据。
英文摘要
Seismic velocity model building (VMB) is fundamental for understanding subsurface structures. Traditional methods demand high-quality starting models and, also, remain limited in resolution in coverage and computationally intensive. Recent generative diffusion model-based approaches capture statistical priors to support traditional inversion methods, but these approaches do not account for the top to bottom progression of information (layer stripping) involved in surface recorded data, where deep velocity information depends on the shallow. To address this issue, we propose a depth-progressive diffusion framework that constructs velocity models incrementally from shallow to deep by propagating prior information. Our method trains on paired shallow-deep velocity patches with variable overlap and explicit depth encoding, integrating multiple geophysical constraints including well logs and seismic images (representing structural information). During inference, we synthesize overlapping depth slices using a progressive algorithm and merge them with Gaussian-weighted blending to eliminate boundary artifacts. This approach leverages both learned geological distributions and observed shallow priors while providing uncertainty quantification. Extensive numerical experiments on in-distribution tests and an out-of-distribution test demonstrate excellent VMB accuracy with a strong correlation between predicted uncertainty and actual errors. As a proof of concept, this part I employs idealized structural constraints derived from vertical reflectivity to validate the methodological framework. The companion paper (Part II) extends the approach to realistic structural constraints relying on migrated images with field data applications.
