Abstract

Influenced by severe ambient noises and nonstationary disturbance signals, multi-class event classification is an enormous challenge in several long-haul application fields of distributed vibration sensing technology (DVS), including perimeter security, railway safety monitoring, pipeline surveillance, etc. In this paper, a deep dual path network is introduced into solving this problem with high learning capacity. The spatial time-frequency spectrum datasets are built by utilizing the multidimensional information of DVS signal, especially the spatial domain information. With the novel datasets and a high-parameter-efficiency network, the proposed scheme presents good reliability and robustness. The feasibility is verified in an actual railway safety monitoring field test, as a proof-of-concept. Seven types of real-life disturbances were implemented and their f1-scores all reached up to 97% in the test. The performance of this proposed approach is fully evaluated and discussed. The presented approach can be employed to improve the performance of DVS in actual applications.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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J. Tejedor, J. M. Guarasa, H. F. Martins, P. G. Juan, M. L. Sonia, P. C. Guillen, D. P. Guy, D. S. Filip, P. Willy, C. H. Ahlen, and G. H. Miguel, “Real Field Deployment of a Smart Fiber-Optic Surveillance System for Pipeline Integrity Threat Detection: Architectural Issues and Blind Field Test Results,” J. Lightwave Technol. 36(4), 1052–1062 (2018).
[Crossref]

P. Jousset, T. Reinsch, T. Ryberg, H. Blanck, A. Clarke, R. Aghayev, G. P. Hersir, J. Henninges, M. Weber, and C. M. Krawczyk, “Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features,” Nat. Commun. 9(1), 2509 (2018).
[Crossref] [PubMed]

2017 (6)

S. Dou, N. Lindsey, A. M. Wagner, T. M. Daley, B. Freifeld, M. Robertson, J. Peterson, C. Ulrich, E. R. Martin, and J. B. Ajo-Franklin, “Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study,” Sci. Rep. 7(1), 11620 (2017).
[Crossref] [PubMed]

M. Aktas, T. Akgun, M. U. Demircin, and D. Buyukaydin, “Deep Learning Based Multi-threat Classication for Phase-OTDR Fiber Optic Distributed Acoustic Sensing Applications,” Proc. SPIE 10208, 102080G (2017).

J. Tejedor, J. Macias-Guarasa, H. F. Martins, D. Piote, J. Pastor-Graells, S. Martin-Lopez, P. Corredera, and M. Gonzalez-Herraez, “A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats,” Sensors (Basel) 17(2), 355–373 (2017).
[Crossref] [PubMed]

D. Iida, K. Toge, and T. Manabe, “Distributed measurement of acoustic vibration location with frequency multiplexed phase-OTDR,” Opt. Fiber Technol. 36, 19–25 (2017).
[Crossref]

J. Zhang, T. Zhu, H. Zheng, K. Yang, M. Liu, and H. Wei, “Breaking through the bandwidth barrier in distributed fiber vibration sensing by sub-Nyquist randomized sampling,” Proc. SPIE 10323, 103238H (2017).

Z. Wang, B. Lu, H. Zheng, Q. Ye, Z. Pan, H. Cai, R. Qu, Z. Fang, and H. Zhao, “Novel railway-subgrade vibration monitoring technology using phase-sensitive OTDR,” Proc. SPIE 10323, 103237G (2017).

2016 (2)

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Z. Wang, L. Zhang, S. Wang, N. Xue, F. Peng, M. Fan, W. Sun, X. Qian, J. Rao, and Y. Rao, “Coherent Φ-OTDR based on I/Q demodulation and homodyne detection,” Opt. Express 24(2), 853–858 (2016).
[Crossref] [PubMed]

2015 (4)

2014 (3)

D. Tan, X. Tian, W. Sun, Y. Zhou, L. Liu, Y. Ma, J. Meng, and H. Zhang, “An Oil & Gas Pipeline Pre-warning System Based on Φ-OTDR,” Proc. SPIE 9157, 91578W (2014).
[Crossref]

Z. Wang, J. Zeng, J. Li, F. Peng, L. Zhang, Y. Zhou, H. Wu, and Y. Rao, “175km Phase-sensitive OTDR with Hybrid Distributed Amplification,” Proc. SPIE 9157, 9157D5 (2014).

H. Wu, Z. Wang, F. Peng, Z. Peng, X. Li, Y. Wu, and Y. Rao, “Field test of a fully-distributed fiber-optic intrusion detection system for long-distance security monitoring of national borderline,” Proc. SPIE 9157, 915790 (2014).
[Crossref]

2012 (1)

2011 (1)

Z. Pan, K. Liang, Q. Ye, H. Cai, R. Qu, and Z. Fang, “Phase-sensitive OTDR system based on digital coherent detection,” Proc. SPIE 8311, 83110S (2011).

2007 (1)

Aghayev, R.

P. Jousset, T. Reinsch, T. Ryberg, H. Blanck, A. Clarke, R. Aghayev, G. P. Hersir, J. Henninges, M. Weber, and C. M. Krawczyk, “Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features,” Nat. Commun. 9(1), 2509 (2018).
[Crossref] [PubMed]

Ahlen, C. H.

Ajo-Franklin, J. B.

S. Dou, N. Lindsey, A. M. Wagner, T. M. Daley, B. Freifeld, M. Robertson, J. Peterson, C. Ulrich, E. R. Martin, and J. B. Ajo-Franklin, “Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study,” Sci. Rep. 7(1), 11620 (2017).
[Crossref] [PubMed]

Akgun, T.

M. Aktas, T. Akgun, M. U. Demircin, and D. Buyukaydin, “Deep Learning Based Multi-threat Classication for Phase-OTDR Fiber Optic Distributed Acoustic Sensing Applications,” Proc. SPIE 10208, 102080G (2017).

Aktas, M.

M. Aktas, T. Akgun, M. U. Demircin, and D. Buyukaydin, “Deep Learning Based Multi-threat Classication for Phase-OTDR Fiber Optic Distributed Acoustic Sensing Applications,” Proc. SPIE 10208, 102080G (2017).

Antonoglou, I.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Bao, X.

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Blanck, H.

P. Jousset, T. Reinsch, T. Ryberg, H. Blanck, A. Clarke, R. Aghayev, G. P. Hersir, J. Henninges, M. Weber, and C. M. Krawczyk, “Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features,” Nat. Commun. 9(1), 2509 (2018).
[Crossref] [PubMed]

Buyukaydin, D.

M. Aktas, T. Akgun, M. U. Demircin, and D. Buyukaydin, “Deep Learning Based Multi-threat Classication for Phase-OTDR Fiber Optic Distributed Acoustic Sensing Applications,” Proc. SPIE 10208, 102080G (2017).

Cai, H.

Z. Wang, B. Lu, H. Zheng, Q. Ye, Z. Pan, H. Cai, R. Qu, Z. Fang, and H. Zhao, “Novel railway-subgrade vibration monitoring technology using phase-sensitive OTDR,” Proc. SPIE 10323, 103237G (2017).

Z. Wang, Z. Pan, Z. Fang, Q. Ye, B. Lu, H. Cai, and R. Qu, “Ultra-broadband phase-sensitive optical time-domain reflectometry with a temporally sequenced multi-frequency source,” Opt. Lett. 40(22), 5192–5195 (2015).
[Crossref] [PubMed]

Z. Pan, K. Liang, Q. Ye, H. Cai, R. Qu, and Z. Fang, “Phase-sensitive OTDR system based on digital coherent detection,” Proc. SPIE 8311, 83110S (2011).

Chen, L.

Chen, Y.

Y. Chen, J. Li, H. Xiao, X. Jin, S. Yan, and J. Feng, “Dual Path Networks,” in 31st Conference on Neural Information Process Systems (NIPS, 2017).

Clarke, A.

P. Jousset, T. Reinsch, T. Ryberg, H. Blanck, A. Clarke, R. Aghayev, G. P. Hersir, J. Henninges, M. Weber, and C. M. Krawczyk, “Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features,” Nat. Commun. 9(1), 2509 (2018).
[Crossref] [PubMed]

Corredera, P.

J. Tejedor, J. Macias-Guarasa, H. F. Martins, D. Piote, J. Pastor-Graells, S. Martin-Lopez, P. Corredera, and M. Gonzalez-Herraez, “A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats,” Sensors (Basel) 17(2), 355–373 (2017).
[Crossref] [PubMed]

Daley, T. M.

S. Dou, N. Lindsey, A. M. Wagner, T. M. Daley, B. Freifeld, M. Robertson, J. Peterson, C. Ulrich, E. R. Martin, and J. B. Ajo-Franklin, “Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study,” Sci. Rep. 7(1), 11620 (2017).
[Crossref] [PubMed]

Demircin, M. U.

M. Aktas, T. Akgun, M. U. Demircin, and D. Buyukaydin, “Deep Learning Based Multi-threat Classication for Phase-OTDR Fiber Optic Distributed Acoustic Sensing Applications,” Proc. SPIE 10208, 102080G (2017).

Dieleman, S.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Doll’ar, P.

S. Xie, R. Girshick, P. Doll’ar, Z. Tu, and K. He, “Aggregated Residual Transformations for Deep Neural Networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2017), pp. 5987–5995.
[Crossref]

Dou, S.

S. Dou, N. Lindsey, A. M. Wagner, T. M. Daley, B. Freifeld, M. Robertson, J. Peterson, C. Ulrich, E. R. Martin, and J. B. Ajo-Franklin, “Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study,” Sci. Rep. 7(1), 11620 (2017).
[Crossref] [PubMed]

Fan, M.

Fan, X.

Fang, Z.

Z. Wang, B. Lu, H. Zheng, Q. Ye, Z. Pan, H. Cai, R. Qu, Z. Fang, and H. Zhao, “Novel railway-subgrade vibration monitoring technology using phase-sensitive OTDR,” Proc. SPIE 10323, 103237G (2017).

Z. Wang, Z. Pan, Z. Fang, Q. Ye, B. Lu, H. Cai, and R. Qu, “Ultra-broadband phase-sensitive optical time-domain reflectometry with a temporally sequenced multi-frequency source,” Opt. Lett. 40(22), 5192–5195 (2015).
[Crossref] [PubMed]

Z. Pan, K. Liang, Q. Ye, H. Cai, R. Qu, and Z. Fang, “Phase-sensitive OTDR system based on digital coherent detection,” Proc. SPIE 8311, 83110S (2011).

Feng, H.

Q. Sun, H. Feng, X. Yan, and Z. Zeng, “Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction,” Sensors (Basel) 15(7), 15179–15197 (2015).
[Crossref] [PubMed]

Feng, J.

Y. Chen, J. Li, H. Xiao, X. Jin, S. Yan, and J. Feng, “Dual Path Networks,” in 31st Conference on Neural Information Process Systems (NIPS, 2017).

Filip, D. S.

Freifeld, B.

S. Dou, N. Lindsey, A. M. Wagner, T. M. Daley, B. Freifeld, M. Robertson, J. Peterson, C. Ulrich, E. R. Martin, and J. B. Ajo-Franklin, “Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study,” Sci. Rep. 7(1), 11620 (2017).
[Crossref] [PubMed]

Girshick, R.

S. Xie, R. Girshick, P. Doll’ar, Z. Tu, and K. He, “Aggregated Residual Transformations for Deep Neural Networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2017), pp. 5987–5995.
[Crossref]

Gonzalez-Herraez, M.

J. Tejedor, J. Macias-Guarasa, H. F. Martins, D. Piote, J. Pastor-Graells, S. Martin-Lopez, P. Corredera, and M. Gonzalez-Herraez, “A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats,” Sensors (Basel) 17(2), 355–373 (2017).
[Crossref] [PubMed]

Graepel, T.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Grewe, D.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Guarasa, J. M.

Guez, A.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Guillen, P. C.

Guy, D. P.

Hassabis, D.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2016), pp. 770–778.

S. Xie, R. Girshick, P. Doll’ar, Z. Tu, and K. He, “Aggregated Residual Transformations for Deep Neural Networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2017), pp. 5987–5995.
[Crossref]

He, Z.

Henninges, J.

P. Jousset, T. Reinsch, T. Ryberg, H. Blanck, A. Clarke, R. Aghayev, G. P. Hersir, J. Henninges, M. Weber, and C. M. Krawczyk, “Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features,” Nat. Commun. 9(1), 2509 (2018).
[Crossref] [PubMed]

Hersir, G. P.

P. Jousset, T. Reinsch, T. Ryberg, H. Blanck, A. Clarke, R. Aghayev, G. P. Hersir, J. Henninges, M. Weber, and C. M. Krawczyk, “Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features,” Nat. Commun. 9(1), 2509 (2018).
[Crossref] [PubMed]

Hinton, G.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Huang, A.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
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D. Tan, X. Tian, W. Sun, Y. Zhou, L. Liu, Y. Ma, J. Meng, and H. Zhang, “An Oil & Gas Pipeline Pre-warning System Based on Φ-OTDR,” Proc. SPIE 9157, 91578W (2014).
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H. Wu, Z. Wang, F. Peng, Z. Peng, X. Li, Y. Wu, and Y. Rao, “Field test of a fully-distributed fiber-optic intrusion detection system for long-distance security monitoring of national borderline,” Proc. SPIE 9157, 915790 (2014).
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J. Zhang, T. Zhu, H. Zheng, K. Yang, M. Liu, and H. Wei, “Breaking through the bandwidth barrier in distributed fiber vibration sensing by sub-Nyquist randomized sampling,” Proc. SPIE 10323, 103238H (2017).

Weinberger, K. Q.

G. Huang, Z. Liu, L. Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2017), pp. 2261–2269.

Willy, P.

Wu, H.

Z. Wang, J. Zeng, J. Li, F. Peng, L. Zhang, Y. Zhou, H. Wu, and Y. Rao, “175km Phase-sensitive OTDR with Hybrid Distributed Amplification,” Proc. SPIE 9157, 9157D5 (2014).

H. Wu, Z. Wang, F. Peng, Z. Peng, X. Li, Y. Wu, and Y. Rao, “Field test of a fully-distributed fiber-optic intrusion detection system for long-distance security monitoring of national borderline,” Proc. SPIE 9157, 915790 (2014).
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Zeng, Z.

Q. Sun, H. Feng, X. Yan, and Z. Zeng, “Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction,” Sensors (Basel) 15(7), 15179–15197 (2015).
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D. Tan, X. Tian, W. Sun, Y. Zhou, L. Liu, Y. Ma, J. Meng, and H. Zhang, “An Oil & Gas Pipeline Pre-warning System Based on Φ-OTDR,” Proc. SPIE 9157, 91578W (2014).
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Zhang, J.

J. Zhang, T. Zhu, H. Zheng, K. Yang, M. Liu, and H. Wei, “Breaking through the bandwidth barrier in distributed fiber vibration sensing by sub-Nyquist randomized sampling,” Proc. SPIE 10323, 103238H (2017).

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Z. Wang, B. Lu, H. Zheng, Q. Ye, Z. Pan, H. Cai, R. Qu, Z. Fang, and H. Zhao, “Novel railway-subgrade vibration monitoring technology using phase-sensitive OTDR,” Proc. SPIE 10323, 103237G (2017).

Zheng, H.

Z. Wang, B. Lu, H. Zheng, Q. Ye, Z. Pan, H. Cai, R. Qu, Z. Fang, and H. Zhao, “Novel railway-subgrade vibration monitoring technology using phase-sensitive OTDR,” Proc. SPIE 10323, 103237G (2017).

J. Zhang, T. Zhu, H. Zheng, K. Yang, M. Liu, and H. Wei, “Breaking through the bandwidth barrier in distributed fiber vibration sensing by sub-Nyquist randomized sampling,” Proc. SPIE 10323, 103238H (2017).

Zhou, Y.

D. Tan, X. Tian, W. Sun, Y. Zhou, L. Liu, Y. Ma, J. Meng, and H. Zhang, “An Oil & Gas Pipeline Pre-warning System Based on Φ-OTDR,” Proc. SPIE 9157, 91578W (2014).
[Crossref]

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J. Zhang, T. Zhu, H. Zheng, K. Yang, M. Liu, and H. Wei, “Breaking through the bandwidth barrier in distributed fiber vibration sensing by sub-Nyquist randomized sampling,” Proc. SPIE 10323, 103238H (2017).

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Z. Wang, J. Zeng, J. Li, F. Peng, L. Zhang, Y. Zhou, H. Wu, and Y. Rao, “175km Phase-sensitive OTDR with Hybrid Distributed Amplification,” Proc. SPIE 9157, 9157D5 (2014).

H. Wu, Z. Wang, F. Peng, Z. Peng, X. Li, Y. Wu, and Y. Rao, “Field test of a fully-distributed fiber-optic intrusion detection system for long-distance security monitoring of national borderline,” Proc. SPIE 9157, 915790 (2014).
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D. Tan, X. Tian, W. Sun, Y. Zhou, L. Liu, Y. Ma, J. Meng, and H. Zhang, “An Oil & Gas Pipeline Pre-warning System Based on Φ-OTDR,” Proc. SPIE 9157, 91578W (2014).
[Crossref]

J. Zhang, T. Zhu, H. Zheng, K. Yang, M. Liu, and H. Wei, “Breaking through the bandwidth barrier in distributed fiber vibration sensing by sub-Nyquist randomized sampling,” Proc. SPIE 10323, 103238H (2017).

Z. Pan, K. Liang, Q. Ye, H. Cai, R. Qu, and Z. Fang, “Phase-sensitive OTDR system based on digital coherent detection,” Proc. SPIE 8311, 83110S (2011).

Z. Wang, B. Lu, H. Zheng, Q. Ye, Z. Pan, H. Cai, R. Qu, Z. Fang, and H. Zhao, “Novel railway-subgrade vibration monitoring technology using phase-sensitive OTDR,” Proc. SPIE 10323, 103237G (2017).

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Z. Wang, L. Li, H. Zheng, J. Liang, X. Wang, B. Lu, Q. Ye, H. Cai, and R. Qu, “Smart Distributed Acoustics/Vibration Sensing with Dual Path Network,” in 26th International Conference on Optical Fiber Sensors (Optical Society of America, 2018), paper WF105.

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Figures (10)

Fig. 1
Fig. 1 (a) The dual path architecture; (b) an equivalent block of RP and (c) an equivalent block of DCP.
Fig. 2
Fig. 2 (a) The spatial time-frequency spectrum and (b) a three-channel disturbance data sample
Fig. 3
Fig. 3 Railway safety monitoring field test.
Fig. 4
Fig. 4 The DVS system schematic.
Fig. 5
Fig. 5 Typical RGB images of disturbances.
Fig. 6
Fig. 6 Accuracy and loss of training dataset during network training.
Fig. 7
Fig. 7 Accuracy and loss of testing datasets at different training steps.
Fig. 8
Fig. 8 Confusion matrix for classification with test dataset.
Fig. 9
Fig. 9 The influence of dataset size on convergence rate with the same batch size.
Fig. 10
Fig. 10 The elapsed time and speeds with different platforms.

Tables (3)

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Table 1 The Network Structures of CNN5 and AlexNet in experiments

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Table 2 Multi-class Events Classification with Test Datasets

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Table 3 Platform configuration

Equations (2)

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S(z,t)= I 2 + Q 2
S t (z:z+MΔz,ω)= m=0 M t t+ t 0 S(z+mΔz,τ) e jωτ dτ

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