用深度学习10毫秒内精准重建激光信号频率，提速百倍并识别地震扰动
Fast frequency reconstruction using Deep Learning for event recognition in ring laser data
- 设计神经网络模型，实现毫秒级频率重建，突破传统傅里叶方法延迟瓶颈
- 在GINGERINO系统中频率估计精度提升2倍，10毫秒内完成数百赫兹信号处理
- 自动分类物理扰动（如地震）准确率达99%~100%，适合实时地质监测场景
从正弦信号中以最小延迟重建频率是多个领域的常见任务，例如环形激光陀螺仪，其输出为拍频信号。传统方法需数秒数据，而本文提出的神经网络方法可在约10毫秒内完成数百赫兹频率的重建，支持快速触发生成。该方法在GINGERINO系统的工作范围内，相比标准傅里叶技术，频率估计精度提升2倍。此外，我们引入自动化分类框架，可识别激光不稳定性与地震事件等物理扰动，在独立测试数据集上地震类别的准确率达99%至100%。这些成果标志着人工智能在地球物理信号分析中的重要进展。
原文摘要 · Abstract (English)
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.
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