Deep Multimodal Learning for Seismoacoustic Fusion to Improve Earthquake-Explosion Discrimination Within the Korean Peninsula

Abstract

Recent geophysical studies have highlighted the potential utility of integrating both seismic and infrasound data to improve source characterization and event discrimination efforts. However, the influence of each of these data types within an integrated framework is not yet well-understood by the geophysical community. To help elucidate the role of each data type within a merged structure, we develop a neural network which fuses seismic and infrasound array data via a gated multimodal unit for earthquake-explosion discrimination within the Korean Peninsula. Model performance is compared before and after adding the infrasound branch. We find that the seismoacoustic model outperforms the seismic model, with the majority of the improvements stemming from the explosions class. The influence of infrasound is quantified by analyzing gated multimodal activations. Results indicate that the model relies comparatively more on the infrasound branch to correct seismic predictions.