Articles | Volume 15, issue 3
https://doi.org/10.5194/essd-15-1389-2023
https://doi.org/10.5194/essd-15-1389-2023
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

Viewed

Total article views: 1,634 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,123 442 69 1,634 53 54
  • HTML: 1,123
  • PDF: 442
  • XML: 69
  • Total: 1,634
  • BibTeX: 53
  • EndNote: 54
Views and downloads (calculated since 09 Nov 2022)
Cumulative views and downloads (calculated since 09 Nov 2022)

Viewed (geographical distribution)

Total article views: 1,634 (including HTML, PDF, and XML) Thereof 1,606 with geography defined and 28 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Jun 2024
Download
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.
Altmetrics
Final-revised paper
Preprint