Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 15, issue 3
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:
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

Viewed

Total article views: 2,037 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,419 528 90 2,037 79 81
  • HTML: 1,419
  • PDF: 528
  • XML: 90
  • Total: 2,037
  • BibTeX: 79
  • EndNote: 81
Views and downloads (calculated since 09 Nov 2022)
Cumulative views and downloads (calculated since 09 Nov 2022)

Viewed (geographical distribution)

Total article views: 2,037 (including HTML, PDF, and XML) Thereof 1,992 with geography defined and 45 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 27 Jan 2025
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.
Read more
Special issue
Benchmark datasets and machine learning algorithms for Earth...
Altmetrics
Final-revised paper
Preprint