A Variational Autoencoder with Semi-Supervised Latent Space for Source Term Estimation

In this paper, we present a bespoke Variational Autoencoder (VAE) architecture focused on identifying critical parameters of hazardous material releases, including source location, release start time, and wind speeds affecting dispersion, from a temporal series of color images. Our VAE architecture is unique in its incorporation of dual latent spaces. The primary latent space is dedicated to the probabilistic inference of source term attributes, along with their associated uncertainties. Concurrently, a secondary latent space is designed to capture salient features of the release that are complementary to the source parameters inferred in the primary space. We benchmark our methodology against a recent deterministic deep learning model using simulated data, demonstrating superior performance across different temporal resolutions. Real-world validation on the Jack Rabbit II Trial 7 experiment confirms the VAE’s ability to estimate key release parameters with reliable uncertainty quantification.