Deep learning methods for modeling infrasound transmission loss in the middle atmosphere

Infrasound are permanently recorded by the International Monitoring System (IMS) set up to detect one kiloton equivalent nuclear explosion around the world (Marty et al. 2019 [2]) and monitor the compliance of the Comprehensive Nuclear T est-Ban-T reaty (CTBT). Accurate modeling of infrasound transmission loss (TL) is essential to interpret microbarometer measurements, evaluate their detection thresholds and characterise wavefield parameters (direction of arrival, velocities, amplitudes, frequencies) and source informations (ground pressure levels associated to earthquakes, acoustic energy from man-made or volcanic explosions). TLs modeling can also help to better characterise the middle atmosphere (MA, 15 100 km) which significantly impact the infrasound propagation. The computational cost of existing numerical propagation modeling tools, such as normal modes or full-waveform simulations (parabolic equations, PEs, Waxler et al. 2021 [3]), does not currently allow the exploration of a wide parameter space (variations in atmospheric states, representation of small-scale variability, frequency and source location) for near-real time TLs predictions; making them unusable within the required CTBT operational framework. Reducing these computation times by neglecting part of the complexity of the propagation phenomenon introduces significant uncertainties in predicted TLs. For example, Le Pichon et al. 2012 [4] proposed an approach relying on heuristic modelling of wave attenuation using a semi-analytical formula mapping wind speeds in the MA to TLs at ground level. However, this method has been optimized on idealized atmospheric models neglecting range-dependent variations in the atmosphere, resulting in large errors for unfavorable initial wind conditions. Artificial intelligence methods are currently explored by Brissaud et al. 2023 [1] in the Norwegian Seismic Array (NORSAR