Deep learning (DL) methods have emerged as a powerful tool for the inversion of geophysical data. When applied to field data, these models often struggle without additional fine-tuning of the network. This is because they are built on the assumption that the statistical patterns in the training and test datasets are the same. To address this, we propose a DL-based inversion scheme for Radio Magnetotelluric data where the subsurface resistivity models are generated using Gaussian Random Fields (GRF). The network's generalization ability was tested with an out-of-distribution (OOD) dataset comprising a homogeneous background and various rectangular-shaped anomalous bodies. After end-to-end training with the GRF dataset, the pre-trained network successfully identified anomalies in the OOD dataset. Synthetic experiments confirmed that the GRF dataset enhances generalization compared to a homogeneous background OOD dataset. The network accurately recovered structures in a checkerboard resistivity model, and demonstrated robustness to noise, outperforming traditional gradient-based methods. Finally, the developed scheme is tested using exemplary field data from a waste site near Roorkee, India. The proposed scheme enhances generalization in a data-driven supervised learning framework, suggesting a promising direction for OOD generalization in DL methods.

翻译：深度学习（DL）方法已成为地球物理数据反演的有力工具。然而，当应用于野外数据时，这些模型通常需要额外的网络微调才能有效工作。这是因为它们建立在训练数据集与测试数据集具有相同统计模式的假设之上。为解决此问题，我们提出了一种基于深度学习的无线电大地电磁数据反演方案，其中地下电阻率模型通过高斯随机场（GRF）生成。网络的泛化能力通过包含均匀背景及多种矩形异常体的分布外（OOD）数据集进行测试。使用GRF数据集进行端到端训练后，预训练网络成功识别了OOD数据集中的异常体。合成实验证实，与均匀背景OOD数据集相比，GRF数据集显著提升了泛化性能。该网络准确重建了棋盘格电阻率模型中的结构，并表现出对噪声的鲁棒性，其性能优于传统基于梯度的方法。最后，使用印度鲁尔基附近废弃物场地的典型野外数据对开发方案进行了验证。所提方案在数据驱动的监督学习框架中增强了泛化能力，为深度学习方法的OOD泛化问题提供了有前景的研究方向。