Towards real-time assessment of infrasound event detection capability using deep learning-based transmission loss estimation
Vers une évaluation temps-réel des capacités de détection d’événements infrasons via l'estimation des pertes de transmission par méthodes d''apprentissage profond
Résumé
Accurate modeling of infrasound transmission loss is essential for evaluating the performance of the International Monitoring System, enabling the effective design and maintenance of infrasound stations to support compliance of the Comprehensive Nuclear-Test-Ban Treaty. State-of-the-art propagation modeling tools enable transmission loss to be finely simulated using atmospheric models. However, the computational cost prohibits the exploration of a large parameter space in operational monitoring applications. To address this, recent studies made use of a deep learning algorithm capable of making transmission loss predictions almost instantaneously. However, the use of nudged atmospheric models leads to an incomplete representation of the medium, and the absence of temperature as an input makes the algorithm incompatible with long range propagation. In this study, we address these limitations by using both wind and temperature fields as inputs to a neural network, simulated up to 130 km altitude and 4, 000 km distance. We also optimize several aspects of the neural network architecture. We exploit convolutional and recurrent layers to capture spatially and range-dependent features embedded in realistic atmospheric models, improving the overall performance. The neural network reaches an average error of 4 dB compared to full parabolic equation simulations and provides epistemic and data-related uncertainty estimates. Its evaluation on the 2022 Hunga Tonga-Hunga Ha’apai volcanic eruption demonstrates its prediction capability using atmospheric conditions and frequencies not included in the training. This represents a significant step towards near real-time assessment of International Monitoring System detection thresh olds of explosive sources. * We provide a convolutional recurrent neural network estimating in near real-time ground-level infrasound transmission loss. * The neural network exploits spatial and range-dependent features in atmospheric models, and makes predictions with an uncertainty estimate. * The network can be used as a tool for near real-time estimation of infrasound event detection capability at a global scale.
* Nous proposons un réseau de neurones récurrent convolutif qui permet d'estimer en temps quasi-réel les pertes par transmission des infrasons au niveau du sol. * Le réseau de neurones exploite les caractéristiques spatiales et dépendante de la distance des modèles atmosphériques d'entrée, et fait des prédictions avec une estimation de l'incertitude. * Le réseau peut être utilisé comme outil pour l'estimation en temps quasi-réel de la capacité de détection d’événements infrasons à l'échelle mondiale.
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