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
本研究将深度渐进扩散框架适配至实际勘探场景，利用偏移属性和层析背景速度替代理想化约束，联合稀疏井数据生成高分辨率速度模型，在合成和现场数据上验证了其精度及对FWI周期跳跃的缓解能力。
AI 中文摘要
全波形反演（FWI）需要准确的初始速度模型以避免周期跳跃，但在实践中构建此类模型仍具挑战性。基于第一部分引入的深度渐进扩散框架（该框架依赖于理想化的反射率约束），本研究将该方法适应于实际勘探场景。我们用从地震图像中提取的偏移导出属性替代完美的结构信息，并引入层析成像得到的平滑背景速度模型作为附加条件输入。该框架联合利用背景/偏移速度、偏移结构信息和稀疏井测量数据，通过深度渐进生成合成高分辨率速度模型。在合成示例上的验证表明，与传统插值和其他深度学习方法相比，该方法具有更高的精度，生成的模型即使在复杂地质构造中也能成功初始化FWI并缓解周期跳跃。现场数据证实了其实用性：尽管仅在合成数据上训练，该方法仍能有效泛化至现场条件，生成的速度模型的合成数据响应几乎与观测地震数据匹配。因此，该框架为在实际约束下部署生成式扩散模型进行速度模型构建提供了一条实用途径。
英文摘要
Full-waveform inversion (FWI) requires accurate initial velocity models to avoid cycle-skipping, but constructing such models remains challenging in practice. Building on the depth-progressive diffusion framework introduced in Part~I, which relied on idealized reflectivity constraints, this work adapts the methodology to realistic exploration scenarios. We replace perfect structural information with migration-derived attributes extracted from seismic images, and introduce smooth background velocity models from tomography as additional conditioning inputs. The framework jointly leverages background/migration velocity, migrated structural information, and sparse well measurements to synthesize high-resolution velocity models through depth-progressive generation. Validation on synthetic examples demonstrates superior accuracy compared to conventional interpolation and alternative deep learning methods, with generated models successfully initializing FWI and mitigating cycle-skipping even in complex geological structures. Field data confirms practical applicability: despite training on synthetic data, the method generalizes effectively to field conditions, producing velocity models with synthetic data response that nearly match observed seismic data. As a result, this framework establishes a practical pathway to deploy generative diffusion models for velocity model building under realistic constraints.
