Fast frequency reconstruction using Deep Learning for event recognition in ring laser data
This addresses the need for rapid trigger generation and automated disturbance identification in geophysical signal analysis, representing an incremental advance in integrating AI into this domain.
They tackled fast frequency reconstruction from sinusoidal signals for event recognition in ring laser gyroscopes, achieving reconstruction within 10 milliseconds and improving frequency estimation precision by a factor of 2 compared to standard methods, while also introducing a classification framework that achieved 99-100% accuracy for seismic events.
The reconstruction of a frequency with minimal delay from a sinusoidal signal is a common task in several fields; for example Ring Laser Gyroscopes, since their output signal is a beat frequency. While conventional methods require several seconds of data, we present a neural network approach capable of reconstructing frequencies of several hundred Hertz within approximately 10 milliseconds. This enables rapid trigger generation. The method outperforms standard Fourier-based techniques, improving frequency estimation precision by a factor of 2 in the operational range of GINGERINO, our Ring Laser Gyroscope.\\ In addition to fast frequency estimation, we introduce an automated classification framework to identify physical disturbances in the signal, such as laser instabilities and seismic events, achieving accuracy rates between 99\% and 100\% on independent test datasets for the seismic class. These results mark a step forward in integrating artificial intelligence into signal analysis for geophysical applications.