Distributed Brillouin frequency shift extraction via a convolutional neural network

Yiqing Chang; Hao Wu; Can Zhao; Li Shen; Songnian Fu; Ming Tang

doi:10.1364/PRJ.389970

[1] P. Lu, N. Lalam, M. Badar, B. Liu, B. T. Chorpening, M. P. Buric, P. R. Ohodnicki. Distributed optical fiber sensing: review and perspective. Appl. Phys. Rev., 6, 041302(2019).

[2] A. Denisov, M. A. Soto, L. Thévenaz. Going beyond 1000000 resolved points in a Brillouin distributed fiber sensor: theoretical analysis and experimental demonstration. Light Sci. Appl., 5, e16074(2016).

[3] X. Bao, L. Chen. Recent progress in Brillouin scattering based fiber sensors. Sensors, 11, 4152-4187(2011).

[4] M. A. Soto, L. Thévenaz. Modeling and evaluating the performance of Brillouin distributed optical fiber sensors. Opt. Express, 21, 31347-31366(2013).

[5] M. A. Farahani, E. Castillo-Guerra, B. G. Colpitts. Accurate estimation of Brillouin frequency shift in Brillouin optical time domain analysis sensors using cross correlation. Opt. Lett., 36, 4275-4277(2011).

[6] C. Zhang, Y. Yang, A. Li. Application of Levenberg-Marquardt algorithm in the Brillouin spectrum fitting. 7th International Symposium on Instrumentation and Control Technology: Optoelectronic Technology and Instruments, Control Theory and Automation, and Space Exploration, 71291Y(2008).

[7] M. A. Farahani, E. Castillo-Guerra, B. G. Colpitts. A detailed evaluation of the correlation-based method used for estimation of the Brillouin frequency shift in BOTDA sensors. IEEE Sens. J., 13, 4589-4598(2013).

[8] A. K. Azad, F. N. Khan, W. H. Alarashi, N. Guo, C. Lu. Temperature extraction in Brillouin optical time-domain analysis sensors using principal component analysis based pattern recognition. Opt. Express, 25, 16534-16549(2017).

[9] H. Wu, L. Wang, N. Guo, C. Shu, C. Lu. Brillouin optical time-domain analyzer assisted by support vector machine for ultrafast temperature extraction. J. Lightwave Technol., 35, 4159-4167(2017).

[10] H. Wu, L. Wang, N. Guo, C. Shu, C. Lu. Support vector machine assisted BOTDA utilizing combined Brillouin gain and phase information for enhanced sensing accuracy. Opt. Express, 25, 31210-31220(2017).

[11] H. Wu, W. Liang, Z. Zhao, C. Shu, L. Chao. Support vector machine based differential pulse-width pair Brillouin optical time domain analyzer. IEEE Photon. J., 10, 6802911(2018).

[12] A. K. Azad, L. Wang, N. Guo, H.-Y. Tam, C. Lu. Signal processing using artificial neural network for BOTDA sensor system. Opt. Express, 24, 6769-6782(2016).

[13] Y. Liang, J. Jiang, Y. Chen, R. Zhu, C. Lu, Z. Wang. Optimized feedforward neural network training for efficient Brillouin frequency shift retrieval in fiber. IEEE Access, 7, 68034-68042(2019).

[14] M. A. Soto, J. A. Ramirez, L. Thevenaz. Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration. Nat. Commun., 7, 10870(2016).

[15] K. Madsen, H. B. Nielsen, O. Tingleff. Methods for Non-Linear Least Squares Problems(2004).

[16] H. H. Aghdam, E. J. Heravi. Guide to Convolutional Neural Networks(2017).

[17] S. Ioffe, C. Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift(2015).

[18] A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 1097-1105(2012).

[19] K. He, X. Zhang, S. Ren, J. Sun. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition, 770-778(2016).

[20] D. P. Kingma, J. Ba. Adam: a method for stochastic optimization(2017).

[21] D. Zhou, Y. Dong, B. Wang, C. Pang, D. Ba, H. Zhang, Z. Lu, H. Li, X. Bao. Single-shot BOTDA based on an optical chirp chain probe wave for distributed ultrafast measurement. Light. Sci. Appl., 7, 32(2018).

[22] C. Zhao, M. Tang, L. Wang, H. Wu, Z. Zhao, Y. Dang, J. Wu, S. Fu, D. Liu, P. P. Shum. BOTDA using channel estimation with direct-detection optical OFDM technique. Opt. Express, 25, 12698-12709(2017).

CLP Journals