Articles | Volume 15, issue 3
Data description paper
24 Mar 2023
Data description paper |  | 24 Mar 2023

DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications

Muhammad Rizwan Asif, Nikolaj Foged, Thue Bording, Jakob Juul Larsen, and Anders Vest Christiansen

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Cited articles

Asif, M. R.: rizwanasif/DL-RMD: DL-RMD (DL-RMD), Zenodo [code],, 2023. 
Asif, M. R., Qi, C., Wang, T., Fareed, M. S., and Khan, S.: License plate detection for multi-national vehicles–a generalized approach, Multimed. Tools Appl., 78, 35585–35606, 2019. 
Asif, M. R., Bording, T. S., Barfod, A. S., Grombacher, D. J., Maurya, P. K., Christiansen, A. V., Auken, E., and Larsen, J. J.: Effect of data pre-processing on the performance of neural networks for 1-D transient electromagnetic forward modelling, IEEE Access, 9, 34635–34646, 2021a. 
Asif, M. R., Bording, T. S., Maurya, P. K., Zhang, B., Fiandaca, G., Grombacher, D. J., Christiansen, A. V., Auken, E., and Larsen, J. J.: A Neural Network-Based Hybrid Framework for Least-Squares Inversion of Transient Electromagnetic Data, IEEE T. Geosci. Remote, 60, 4503610,, 2021b. 
Asif, M. R., Foged, N., Bording, T., Larsen, J. J., and Christiansen, A. V.: DL-RMD: A geophysically constrained electromagnetic resistivity model database for deep learning applications, Zenodo [data set],, 2022a. 
Short summary
To apply a deep learning (DL) algorithm to electromagnetic (EM) methods, subsurface resistivity models and/or the corresponding EM responses are often required. To date, there are no standardized EM datasets, which hinders the progress and evolution of DL methods due to data inconsistency. Therefore, we present a large-scale physics-driven model database of geologically plausible and EM-resolvable subsurface models to incorporate consistency and reliability into DL applications for EM methods.
Final-revised paper