Speaker
Description
GINGER data analysis is based on the experience gained with GINGERINO data analysis, the general analysis scheme will be described.
The reconstruction of the beat frequency of a laser gyroscope signal in the shortest possible time is a non-trivial challenge. Advancements in artificial intelligence are used to develop a DAQ system capable of determining the beat signal frequency with higher precision than the FFT-based algorithm, achieving a delay time of just one-hundredth of a second. This neural network achieves double the precision compared to the FFT algorithm. The reconstructed signal is then classified by exploiting the relationship between laser physics phenomena and fringe contrast monitoring. Furthermore, a neural network created for seismic event recognition has, trained on real events present in GINGERINO's data, achieved an accuracy on the available test data ranging between 99% and 100%.
| AI keywords | Anomaly detection; fast frequency reconstruction; seismic event recognition; minimal time delay |
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