发表机构
Stanford University; Mineral-X; Xcalibur Smart Mapping（斯坦福大学; Mineral-X; Xcalibur Smart Mapping）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对三维航空电磁贝叶斯反演正演成本过高的问题，开发持续学习神经算子代理模型，结合集成分歧有效性检查，实现调查尺度下带不确定性量化的电导率成像，大幅提升计算效率。
AI 中文摘要
时域航空电磁（AEM）数据的三维概率反演受限于正演求解的成本。即使单次模拟仅需数十秒，但对数百万个测点的调查进行贝叶斯反演需要约10^10次正演评估。为解决该问题，我们开发了三维AEM正演算子的持续学习神经算子代理模型，以替代贝叶斯反演内部的求解器。首先，我们认为无论指定何种地质先验，麦克斯韦定律保持不变；其次，我们通过对连续先验进行持续学习，避免了在单一先验上学习的局限，这意味着我们的代理模型在未来案例研究（无论由作者还是科学界开展）中应用时会变得更丰富。我们采用基于集成分歧的有效性检查，将测量值超出训练范围的案例导向求解器。在代理模型的驱动下，相同的马尔可夫链蒙特卡罗采样器能复现全求解器的后验分布，其可信区间覆盖真值的误差在2.6个百分点以内。将其应用于西澳大利亚2013年Capricorn TEMPEST调查时，该代理模型在数秒内完成了超过200万个测点的反演，这一计算量对求解器而言是不可行的，针对整个调查测试地质先验仅需数分钟。该框架可在调查尺度上提供带不确定性量化的电导率成像，我们认为这对利用地球物理技术开展近实时矿物系统定位至关重要。
英文摘要
Three-dimensional probabilistic inversion of time-domain airborne electromagnetic (AEM) data is limited by the cost of the forward solve. Even though one simulation takes only tens of seconds, a Bayesian inversion of a survey of millions of soundings requires of order $10^{10}$ forward evaluations. To address this, we develop a continually learning neural-operator surrogate of the three-dimensional AEM forward operator that replaces the solver inside the Bayesian inversion. We start from the point of view that regardless of what geological prior is specified, Maxwell's laws remain invariant. Secondly, we avoid the limitation of learning on a single prior by continual learning on consecutive priors, which means our surrogate becomes richer as it is applied in future case studies, either by the authors, or by the scientific community. We use a validity check built on ensemble disagreement to divert cases with measurements outside the training range to the solver. Driven by the surrogate, the identical Markov chain Monte Carlo sampler reproduces the full-solver posterior, and its credible intervals cover the truth within 2.6 percentage points. Applied to the 2013 Capricorn TEMPEST survey in Western Australia, the surrogate inverts over two million soundings in seconds, a computation infeasible for the solver. Testing the geological prior against the entire survey costs minutes. The framework delivers uncertainty-quantified conductivity imaging at survey scale, which we believe is essential to perform near real-time mineral-systems targeting with geophysics.
