Public articles linked to the same research event.
arXiv The authors propose a physics-guided multi-modal variational autoencoder (PG-VAE) that uses a multi-stream fusion strategy to map independent latent features from raw shot gathers and spectral-dispersion images, a decoder with learned transposed convolutions and residual refinement to bias predictions toward layered earth models, and a differentiable surrogate network as a forward-consistency regularizer penalizing predictions that violate Rayleigh-wave dispersion physics; the network is trained on synthetic data from elastic modeling, validated on unseen synthetic data, and then evaluated on an ultra-high-density field dataset from the Devine test site in Texas, with Grad-CAM used to examine input features contributing to predictions.
The authors propose a physics-guided multi-modal variational autoencoder (PG-VAE) that uses a multi-stream fusion strategy to map independent latent features from raw shot gathers and spectral-dispersion images, a decoder with learned transposed convolutions and residual refinement to bias predictions toward layered earth models, and a differentiable surrogate network as a forward-consistency regularizer penalizing predictions that violate Rayleigh-wave dispersion physics; the network is trained on synthetic data from elastic modeling, validated on unseen synthetic data, and then evaluated on an ultra-high-density field dataset from the Devine test site in Texas, with Grad-CAM used to examine input features contributing to predictions.
The authors propose a physics-guided multi-modal variational autoencoder (PG-VAE) that uses a multi-stream fusion strategy to map independent latent features from raw shot gathers and spectral-dispersion images, a decoder with learned transposed convolutions and residual refinement to bias predictions toward layered earth models, and a differentiable surrogate network as a forward-consistency regularizer penalizing predictions that violate Rayleigh-wave dispersion physics; the network is trained on synthetic data from elastic modeling, validated on unseen synthetic data, and then evaluated on an ultra-high-density field dataset from the Devine test site in Texas, with Grad-CAM used to examine input features contributing to predictions.
The authors propose a physics-guided multi-modal variational autoencoder (PG-VAE) that uses a multi-stream fusion strategy to map independent latent features from raw shot gathers and spectral-dispersion images, a decoder with learned transposed convolutions and residual refinement to bias predictions toward layered earth models, and a differentiable surrogate network as a forward-consistency regularizer penalizing predictions that violate Rayleigh-wave dispersion physics; the network is trained on synthetic data from elastic modeling, validated on unseen synthetic data, and then evaluated on an ultra-high-density field dataset from the Devine test site in Texas, with Grad-CAM used to examine input features contributing to predictions.