Preprints
https://doi.org/10.5194/essd-2022-345
https://doi.org/10.5194/essd-2022-345
 
09 Nov 2022
09 Nov 2022
Status: this preprint is currently under review for the journal ESSD.

DL-RMD: A geophysically constrained electromagnetic resistivity model database for deep learning applications

Muhammad Rizwan Asif1,3,4, Nikolaj Foged1,4, Thue Bording1,2, Jakob Juul Larsen3,4, and Anders Vest Christiansen1,4 Muhammad Rizwan Asif et al.
  • 1Hydro-Geophysics Group (HGG), Department of Geoscience, Aarhus University, Aarhus C, 8000, Denmark
  • 2Aarhus GeoInstruments, Åbyhøj, 8230, Denmark
  • 3Department of Electrical and Computer Engineering, Aarhus University, Aarhus N, 8200, Denmark
  • 4Aarhus University Centre for Water Technology (WATEC), Aarhus N, 8200, Denmark

Abstract. Deep learning algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale data sets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database of a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. The presented database can potentially be used to build surrogate models of well-known processes and aid in labour intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, it will incorporate consistency and credibility in deep learning models. We show the effectiveness of the presented database by surrogating the forward modelling process, and urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database. The dataset is publically available at https://doi.org/10.5281/zenodo.7260886 (Asif et al., 2022a).

Muhammad Rizwan Asif et al.

Status: open (until 04 Jan 2023)

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Muhammad Rizwan Asif et al.

Data sets

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

Muhammad Rizwan Asif et al.

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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 sub-surface models to incorporate consistency and reliability in DL applications for EM methods.