A Neural Network-Based Hybrid Framework for Least-Squares Inversion of Transient Electromagnetic Data

Inversion of large-scale time-domain transient electromagnetic (TEM) surveys is computationally expensive and time-consuming. The calculation of partial derivatives for the Jacobian matrix is by far the most computationally intensive task, as this requires calculation of a significant number of forw...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-10
Hauptverfasser:	Asif, Muhammad Rizwan, Bording, Thue S., Maurya, Pradip K., Zhang, Bo, Fiandaca, Gianluca, Grombacher, Denys J., Christiansen, Anders V., Auken, Esben, Larsen, Jakob Juul
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Artificial neural networks Computational modeling Computer applications Computing time Conductivity Data models Forward modeling Frameworks Geology inverse modeling Jacobi matrix method Jacobian matrices Jacobian matrix Least squares Logic gates Mathematical model Model accuracy Modelling Neural networks Neurons Surveys Tolerances Tolerances (dimensional) Training transient electromagnetics (TEM)
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