OpenEM: Large-scale multi-structure 3D dataset for electromagnetic methods

Electromagnetic methods are among the most widely used techniques in the geophysical exploration industry due to their efficiency and non-invasive nature. However, their data processing workflows are highly time-consuming and strongly dependent on expert intervention. With the rapid and broad success of deep learning, applying deep learning techniques to electromagnetic methods to overcome the limitations of traditional approaches has become an active area of research. The effectiveness of deep learning methods, however, largely depends on the quality of the dataset, which directly influences model performance and generalization capability. Existing applications typically rely on self-constructed datasets composed of randomly generated one-dimensional models or structurally simple three-dimensional models, which fail to capture the complexity of realistic geological environments. Moreover, the absence of a unified and publicly available three-dimensional geoelectrical model repository has further constrained the development of deep learning for three-dimensional electromagnetic exploration. To address these challenges, we introduce OpenEM, a large-scale, multi-structural three-dimensional geoelectrical model repository that incorporates a wide range of geologically plausible subsurface structures.

OpenEM comprises nine categories of geoelectrical models, encompassing a wide spectrum of subsurface structures ranging from simple to complex. These include models of homogeneous half-spaces with embedded anomalous bodies, as well as configurations featuring flat stratigraphy, curved stratigraphy, planar faults, curved faults, and their variants containing anomalous bodies. The resistivity values span from 1 to 2000 Ω·m, with the number of layers ranging from three to seven. In models containing anomalous bodies, the number of anomalies varies from one to five, and both regular and irregular geometries are considered to enhance dataset diversity and realistic representativeness. In addition, OpenEM is accompanied by a three-dimensional model generator that enables fully controllable model construction, allowing users to customize structural configurations, including resistivity magnitudes, fault geometries and locations, as well as the size, shape, and placement of anomalous bodies.

OpenEM provides a unified, comprehensive, and large-scale dataset for common electromagnetic exploration systems, thereby promoting the application of deep learning methods in electromagnetic prospecting.