Enhancing seismic monitoring with Virtual Seismic Arrays: Application of a deep learning framework

Seismic arrays are essential for collecting and analyzing seismic data, significantly enhancing our understanding of geophysical processes such as the localization of seismic sources. We introduce the concept of Virtual Seismic Arrays, where the array recordings are predicted from a single reference station, removing the need for continuous deployment of all array stations. This work builds on the research by Klinge et al. (2025), which introduced a Deep Learning approach using encoder-decoder networks to learn and predict transfer properties between two seismic stations. By training the algorithm on data of the Gräfenberg array in the secondary microseism frequency band, we develop models that effectively capture the transfer characteristics between a chosen reference station and each of the other stations within the array. To evaluate how well the models represent the underlying wave propagation, we use beamforming and apply it to both the original data from all stations and the corresponding predictions generated by the models. We assess two scenarios: one where the dominant backazimuths and slownesses are consistent with the training dataset, and another where the models are applied to data from different conditions. Our results show strong agreement between the predicted and original beamforming results, demonstrating the potential of Virtual Seismic Arrays for future application.