标题： 机器学习在航空电磁数据反演中的应用：一种自举方法 显示英文标题

标题： Machine Learning for Airborne Electromagnetic Data Inversion: a Bootstrapped Approach

摘要： 基于飞机的调查以收集航空电磁数据是成像地球表面大范围区域的关键方法，旨在更好地了解含水层系统。 尽管经过多年的发展，三维反演在计算需求、正则化选择、超参数调整和实时反演方面仍然存在挑战。 我们提出了一种新的航空电磁数据反演方法，该方法利用机器学习克服传统三维反演方法的计算负担，该方法隐式地包含学习到的正则化，并适用于实时反演。 该方法结合一维反演结果与地质统计建模，创建定制的训练数据集，从而开发出一种专门的神经网络，能够从航空电磁数据预测二维电导率模型。 该方法需要事先进行三维正演建模和一维反演，但在推理过程中不需要正演建模。 该工作流程应用于加利福尼亚州的Kaweah次盆地，成功重建了与实际数据和地质钻孔信息一致的电导率模型。 结果突显了该方法在提供快速且精确的地下成像方面的潜力，为地下水勘探和其他近地表应用提供了有价值的工具。 显示英文摘要

摘要： Aircraft-based surveying to collect airborne electromagnetic data is a key method to image large swaths of the Earth's surface in pursuit of better knowledge of aquifer systems. Despite many years of advancements, 3D inversion still poses challenges in terms of computational requirements, regularization selection, hyperparameter tuning and real-time inversion. We present a new approach for the inversion of airborne electromagnetic data that leverages machine learning to overcome the computational burden of traditional 3D inversion methods, which implicitly includes learned regularization and is applicable in real-time. The method combines 1D inversion results with geostatistical modeling to create tailored training datasets, enabling the development of a specialized neural network that predicts 2D conductivity models from airborne electromagnetic data. This approach requires 3D forward modeling and 1D inversion up front, but no forward modeling during inference. The workflow is applied to the Kaweah Subbasin in California, where it successfully reconstructs conductivity models consistent with real-world data and geological drill hole information. The results highlight the method's capability to deliver fast and accurate subsurface imaging, offering a valuable tool for groundwater exploration and other near-surface applications.