DL-RMD: A geophysically constrained electromagnetic resistivity model database for deep learning applications
This repository provides the trained network weights for the forward modelling of tTEM data and make a network forward pass to obtain the tTEM forward response from the network.
The original files of the dataset can be downloaded from here (comma separated delimiters and each row is one entry). The metadata is as below:
S_RMD_rho.txt contains the resistivity values for the shallow TEM systems.
S_RMD_thk.txt contains the thickness of each resistivity layer for the shallow TEM systems.
S_RMD_EMresponse.txt contains the forward responses for the corresponding resistiity models in S_RMD_rho.txt.
S_VK_rho.txt contains the resistivity values von Kármán models (90 layers) used for the shallow TEM systems.
S_VK_thk.txt contains the thickness of each resistivity layer of von Kármán models (90 layers) used for the shallow TEM systems.
I_RMD_rho.txt contains the resistivity values for the intermediate depth TEM systems.
I_RMD_thk.txt contains the thickness of each resistivity layer for the intermediate depth TEM systems.
I_VK_rho.txt contains the resistivity values von Kármán models (90 layers) used for the intermediate depth TEM systems.
I_VK_thk.txt contains the thickness of each resistivity layer of von Kármán models (90 layers) used for the intermediate depth TEM systems.
D_RMD_rho.txt contains the resistivity values for the deep TEM systems.
D_RMD_thk.txt contains the thickness of each resistivity layer for the deep TEM systems.
D_VK_rho.txt contains the resistivity values von Kármán models (90 layers) used for the deep TEM systems.
D_VK_thk.txt contains the thickness of each resistivity layer of von Kármán models (90 layers) used for the deep TEM systems.
Asif, M. R., Foged, N., Bording, T., Larsen, J. J., and Christiansen, A. V. (2023). DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications. Earth System Science Data, 15, 1389–1401. https://doi.org/10.5194/essd-15-1389-2023
Asif, M. R., Foged, N., Bording, T., Larsen, J. J., and Christiansen, A. V. (2022). DL-RMD: A geophysically constrained electromagnetic resistivity model database for deep learning applications (Dataset) (Version v3) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7260886
Asif, M. R., Foged, N., Maurya, P.K., Grombacher, D.J., Christiansen, A. V., Auken, E., and Larsen, J.J. (2022). Integrating neural networks in least-squares inversion of airborne time-domain electromagnetic data. Geophysics, vol. 87(4), E177-E187. https://doi.org/10.1190/geo2021-0335.1
Asif, M. R., Maurya, P.K., Christiansen, A. V., Larsen, J.J. and Auken, E. (2022). Deep learning based expert system to automate time-domain electromagnetic data processing, 34th Symposium on the Application of Geophysics to Engineering and Environmental Problems, SAGEEP 2022.
Asif, M. R., Maurya, P.K., Foged, N., Larsen, J.J., Auken, E., and Christiansen, A. V. (2022). Automated transient electromagnetic data processing for ground-based and airborne systems by a deep learning expert system. IEEE Transactions on Geoscience and Remote Sensing, in review.