Jana Klinge , Sven Schippkus, Jan Walda, Celine Hadziioannou, Dirk Gajewski

Wouldn't it be beneficial if we could predict the time series at a seismic station even if the station no longer exists? In geophysical data analysis, this capability would enhance our ability to study and monitor seismic events and seismic noise, particularly in regions with incomplete station coverage or where stations are temporarily offline. This study introduces a novel adaption of encoder-decoder networks from the subfield of Deep Learning, modified to predict the development of seismic wave fields between two seismic stations. Using one-dimensional time series measurements, our algorithm aims to learn and predict signal transformations between the two stations by approximating the transfer function. Initially, we evaluate this proof of concept in a simplified controlled setting using synthetic data, before we incorporate field data gathered at a seismic exploration site in an area containing several roads, wind turbines, oil pump jacks and railway traffic. Across diverse scenarios, the model demonstrates proficiency in learning the transfer function among various seismic station configurations. Particularly, it achieves high accuracy in predicting a majority of seismic wave phases across different datasets. Diverging significantly from encoder-decoder networks that estimate time series forecasts by analysing historical trends, our approach places greater emphasis on the wave propagation between nearby locations. Thereby, the analysis incorporates both phase and amplitude information and provides a new approach to approximate the transfer function relying on Machine Learning techniques. The gained knowledge enables to reconstruct data from missing, offline, or defunct stations in the context of temporary seismic arrays or exclude non-relevant data for denoising.