Predicting Atmospheric Effective Sound Speed Using Synthetic Infrasound and Machine Learning

Abstract We introduce a machine learning (ML) model that reconstructs atmospheric conditions at the location of an explosive source using regionally recorded synthetic infrasound. A convolutional neural network (CNN) processes full waveforms and source‐receiver geometries to estimate the vertical profile of effective sound speed. The model is trained on synthetic data generated by propagating a source time function through modeled atmospheres out to random station locations. Prediction accuracy is high with an average root mean squared error (RMSE) of 8 m/s. We find that some poorly predicted profiles nonetheless reproduce the original waveforms with high accuracy, indicating a nonuniqueness between atmospheric conditions and recorded waveforms. Unlike traditional physics‐based inversions that rely on array deployments, our ML model can analyze single‐channel data and, once trained, makes predictions within milliseconds. Given the performance on synthetic data, future tests should include real‐world data to further assess the benefits of ML‐based frameworks for infrasound analysis.