Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis

To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential.

Automated summary

This study presents the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. A convolutional neural network (CNN)-based signal post-processing method is proposed to enhance temperature extraction and system performance, shortening measurement time by more than nine times.