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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-345', Anonymous Referee #1, 10 Dec 2022
    • AC1: 'Reply on RC1', Muhammad Rizwan Asif, 10 Jan 2023
  • RC2: 'Comment on essd-2022-345', Anonymous Referee #2, 12 Dec 2022
    • AC2: 'Reply on RC2', Muhammad Rizwan Asif, 10 Jan 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Muhammad Rizwan Asif on behalf of the Authors (06 Feb 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (21 Feb 2023) by Martin Schultz
AR by Muhammad Rizwan Asif on behalf of the Authors (27 Feb 2023)  Author's response   Manuscript 
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