将概念地质信息引入贝叶斯层析成像
Introducing Conceptual Geological Information into Bayesian Tomographic Imaging
AI总结：
本研究通过将辫状河系统和海洋准层序等地质概念信息作为先验注入贝叶斯推断，利用MDN近乎瞬时地增强层析成像后验分布，提升图像地质合理性并实现概念模型推断。
摘要：
地质过程模型模拟一系列动态过程，将基础地形演化为最终的2D剖面或3D地质场景。原则上，可以更新过程参数以更好地与观测到的地球物理或地质数据对齐；然而，体现不同概念模型的许多过程模型实现可能与观测数据提供相似的一致性，并且由于该任务的计算需求，找到所有此类实现可能是不可行的。或者，可以使用地球物理概率层析方法来估计与从先前实验中获得的信息和新数据（贝叶斯后验分布）一致的目标地下结构模型族。然而，该模型族很少体现地质上合理的图像。我们表明，通过将地质先验信息注入贝叶斯推断，可以增强层析图像的后验分布，并且我们可以通过训练的MDN近乎瞬时地做到这一点。我们引入两个地质概念作为地质先验信息：一个辫状河系统，和一组海洋准层序，每个都由GAN参数化。然后可以使用目标结构的数据，使用MDN推断使用任一地质概念的图像参数值。我们的MDN解决方案与使用昂贵的McMC方法找到的解决方案非常相似，并且虽然使用不正确的地质概念模型提供不太准确的结果，但平均结构仍然近似目标。然后我们表明，分类网络可以推断正确的地质概念模型。因此，与没有地质先验信息获得的图像相比，施加甚至不正确的地质先验信息可以改善地球物理层析图像，并且原则上可以直接从地球物理旅行时数据推断地质概念模型。
英文摘要：
Geological process models simulate a range of dynamic processes to evolve a base topography into a final 2D cross-section or 3D geological scenario. In principle, process parameters may be updated to better align with observed geophysical or geological data; however, many realisations of process models that embody different conceptual models may provide similar consistency with observed data, and finding all such realisations may be infeasible due to the computational demands of the task. Alternatively, geophysical probabilistic tomographic methods may be used to estimate the family of models of a target subsurface structure that are consistent both with information obtained from previous experiments and with new data (the Bayesian posterior distribution). However, this family seldom embodies geologically reasonable images. We show that the posterior distribution of tomographic images can be enhanced by injecting geological prior information into Bayesian inference, and we can do this near-instantaneously by trained MDNs. We invoke two geological concepts as geological prior information: a braided river system, and a set of marine parasequences, each parameterised by a GAN. Data from a target structure can then be used to infer the image parameter values using either geological concept using MDNs. Our MDN solutions closely resemble those found using expensive McMC methods, and while the use of incorrect geological conceptual models provides less accurate results the mean structures still approximate the target. We then show that a classification network can infer the correct geological conceptual model. Thus, imposing even incorrect geological prior information may improve geophysical tomographic images compared to those obtained without geological prior information, and in principle geological conceptual models can be inferred directly from geophysical travel time data.
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