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

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

doi:https://doi.org/10.5194/essd-15-1389-2023

Articles | Volume 15, issue 3

https://doi.org/10.5194/essd-15-1389-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Benchmark datasets and machine learning algorithms for Earth...

https://doi.org/10.5194/essd-15-1389-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Data sets

DL-RMD: A geophysically constrained electromagnetic resistivity model database for deep learning applications (Dataset) Muhammad Rizwan Asif, Nikolaj Foged, Thue Bording, Jakob Juul Larsen, and Anders Vest Christiansen https://doi.org/10.5281/zenodo.7260886

rizwanasif/DL-RMD: DL-RMD (DL-RMD) Muhammad Rizwan Asif https://doi.org/10.5281/zenodo.7740243

An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data (https://hgg.au.dk/software/aarhusinv) Esben Auken, Anders Vest Christiansen, Casper Kirkegaard, Gianluca Fiandaca, Cyril Schamper, Ahmad Ali Behroozmand, Andrew Binley, Emil Nielsen, Flemming Effersø, Niels Bøie Christensen, Kurt Sørensen, Nikolaj Foged, and Giulio Vignoli https://doi.org/10.1071/EG13097

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.