Magnetotelluric Data Inversion Using Subdomain Encoding Scheme with Variational Autoencoder

A 2D inversion scheme based on variational autoen-coder (VAE) is applied to magnetotelluric (MT) data inversion. Trained with the carefully designed data set, the VAE can reparameterize the subsurface model with latent variables in an adaptive manner. To improve the computational efficiency and the...

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Veröffentlicht in:Digest - IEEE Antennas and Propagation Society. International Symposium (1995) S. 255 - 256
Hauptverfasser: Zhou, Hongyu, Guo, Rui, Li, Maokun, Yang, Fan, Xu, Shenheng, Abubakar, Aria
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 23.07.2023
Schlagworte:
Adaptation models
Computational modeling
Costs
Data models
Encoding
Magnetic domains
Training
ISSN:1947-1491
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Zusammenfassung:A 2D inversion scheme based on variational autoen-coder (VAE) is applied to magnetotelluric (MT) data inversion. Trained with the carefully designed data set, the VAE can reparameterize the subsurface model with latent variables in an adaptive manner. To improve the computational efficiency and the generalization ability of VAE, the subsurface model is divided into subdomains, all encoded with the same VAE. In the inversion, the latent variables of each sub domain are inverted with the Gauss-Newton method. This method can flexibly incorporate prior knowledge into inversion. Numerical experiments show that the reconstructed models are more accurate and better consistent with prior knowledge than traditional inversion in the spatial domain.
ISSN:1947-1491
DOI:10.1109/USNC-URSI52151.2023.10237551