Abstract. Deep learning algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale data sets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database of a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. The presented database can potentially be used to build surrogate models of well-known processes and aid in labour intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, it will incorporate consistency and credibility in deep learning models. We show the effectiveness of the presented database by surrogating the forward modelling process, and urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database. The dataset is publically available at https://doi.org/10.5281/zenodo.7260886 (Asif et al., 2022a).

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DL-RMD：用于深度学习应用的地球物理约束电磁电阻率模型数据库

摘要。深度学习算法在许多应用中都显示出令人难以置信的潜力。这些需要大量数据的方法的成功很大程度上与大规模数据集的可用性有关，因为通常需要数百万次观察才能达到可接受的性能水平。最近，人们越来越关注将深度学习方法应用于地球物理应用，其中使用电磁方法通过观察地下材料电阻率的变化来绘制地下地质图。迄今为止，没有针对电磁方法的标准化数据集，由于数据不一致，这阻碍了深度学习算法的进展、评估、基准测试和演进。所以，我们提出了一个大规模电阻率模型数据库，该数据库包含各种地质上合理和地球物理可解析的地下结构，用于通常部署的陆基和机载电磁系统。所呈现的数据库有可能用于构建知名流程的代理模型并帮助完成劳动密集型任务。该数据库的地球物理约束属性不仅将实现增强的性能和改进的泛化能力，更重要的是，它将在深度学习模型中融入一致性和可信度。我们通过替代正演建模过程来展示所提供数据库的有效性，并敦促对电磁方法深度学习感兴趣的地球物理界利用所提供的数据库。该数据集在 https 上公开可用：