ROTDR signal enhancement via deep convolutional denoising autoencoders trained with domain randomization
EstadísticasView Usage Statistics
Full recordShow full item record
AuthorLaarossi, Ismail; Pardo Franco, Arturo; Conde Portilla, Olga María; Quintela Incera, María Ángeles; López Higuera, José Miguel
© 2019 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Proceedings of SPIE, 2019, 11199, 111993N
Seventh European Workshop on Optical Fibre Sensors (EWOFS 2019), Limassol, Chipre, 2019
SPIE Society of Photo-Optical Instrumentation Engineers
Enlace a la publicación
Spontaneous raman scattering
Fiber optic sensors
Raman distributed temperature sensors
In this work, a deep convolutional adaptive filter is proposed to enhance the performance of a Raman based distributed temperature sensor system by the application of domain randomization methods for its training. The improvement of the signal-to-noise ratio in the Raman backscattered signals in the training process and translation to a real scenario is demonstrated. The ability of the proposed technique to reduce signal noise effectively is proved independently of the sensor configuration and without degradation of temperature accuracy or spatial resolution of these systems. Moreover, using single trace to noise reduction in the ROTDR signals accelerates the system response avoiding the employment of many averages in a unique measurement.