发表机构
University of Edinburgh（爱丁堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对贝叶斯走时层析成像中标准潜空间反演无法处理流形外真实地球模型的问题，提出一种以生成流形为引导的贝叶斯模型空间反演方法，可成功恢复流形外复杂地质特征并保持低解不确定性。
AI 中文摘要
地球物理反问题的解通常不唯一，且能从额外的地质先验信息中大幅受益。生成神经网络（Generative Neural Networks，GNNs）常被用于参数化这类信息，通常将解限制在低维潜空间中。最终的反演结果通过GNNs将潜空间参数映射回原始高维模型空间得到，但此时解仍被限制在低维地质生成流形上；若真实地球模型位于该流形之外，标准潜空间反演会失效。我们提出一种贝叶斯模型空间反演方法，利用生成流形作为引导来寻找位于该流形之外的解。我们在简单和更复杂的地质结构上开展的非线性走时层析成像实验中验证了该方法。通过将我们的方法与使用均匀先验概率的模型空间反演、标准潜空间反演进行比较，我们发现信息较少的均匀先验无法约束解，而当真实地球模型位于生成流形之外时，潜空间方法会失效。相反，我们的新型模型空间反演方法成功恢复了复杂的流形外地质特征，同时保持了较低的解不确定性。
英文摘要
Solutions to geophysical inverse problems are generally non-unique, and greatly benefit from additional geological prior information. Generative Neural Networks (GNNs) are commonly used to parametrise such information, typically restricting solutions to a lower-dimensional latent space. The final inversion result is obtained by mapping the latent space parameters to the original higher-dimensional model space using the GNNs. However, solutions then remain confined to a low-dimensional geological generative manifold; if the true Earth model lies outside of this manifold, standard latent space inversion fails. We propose a Bayesian model space inversion method that uses the generative manifold as a guide to find the solutions that lie outside this manifold. We validate our approach in non-linear travel-time tomography experiments on both simple and more geological structures. By comparing our method with model space inversion using uniform prior probabilities and standard latent-space inversion, we demonstrate that the less informative uniform prior fails to constrain the solution, while the latent space approach fails when the true Earth model lies outside of the generative manifold. Conversely, our new model-space inversion method successfully recovers complex off-manifold geological features while maintaining low solution uncertainty.
DOI：10.3997/2214-4609.202610891
