## Efficient dynamic events discrimination technique for fiber distributed Brillouin sensors |

Optics Express, Vol. 19, Issue 20, pp. 18917-18926 (2011)

http://dx.doi.org/10.1364/OE.19.018917

Acrobat PDF (1860 KB)

### Abstract

A technique to detect real time variations of temperature or strain in Brillouin based distributed fiber sensors is proposed and is investigated in this paper. The technique is based on anomaly detection methods such as the RX-algorithm. Detection and isolation of dynamic events from the static ones are demonstrated by a proper processing of the Brillouin gain values obtained by using a standard BOTDA system. Results also suggest that better signal to noise ratio, dynamic range and spatial resolution can be obtained. For a pump pulse of 5 ns the spatial resolution is enhanced, (from 0.541 m obtained by direct gain measurement, to 0.418 m obtained with the technique here exposed) since the analysis is concentrated in the variation of the Brillouin gain and not only on the averaging of the signal along the time.

© 2011 OSA

## 1. Introduction

1. C. Galindez and J. M. Lopez-Higuera, “Decimeter spatial resolution by using differential pre-excitation BOTDA pulse technique,” IEEE Sens. J. **PP**(99), 1–1 (2011). [CrossRef] [PubMed]

3. K. Y. Song, Z. He, and K. Hotate, “Distributed strain measurement with millimeter-order spatial resolution based on Brillouin optical correlation domain analysis,” Opt. Lett. **31**(17), 2526–2528 (2006). [CrossRef] [PubMed]

4. M. A. Soto, G. Bolognini, and F. Di Pasquale, “Long-range simplex-coded BOTDA sensor over 120 km distance employing optical preamplification,” Opt. Lett. **36**(2), 232–234 (2011). [CrossRef] [PubMed]

5. A. Zornoza, A. Minardo, R. Bernini, A. Loayssa, and L. Zeni, “Pulsing the probe wave to reduce nonlocal effects in Brillouin optical time-domain analysis sensors,” IEEE Sens. J. **11**, 1067–1068 (2011). [CrossRef]

6. T. Horiguchi, T. Kurashima, and M. Tateda, “Technique to measure distributed strain in optical fibers,” IEEE Photon. Technol. Lett. **2**(5), 352–354 (1990). [CrossRef]

7. T. Kurashima, T. Horiguchi, and M. Tateda, “Distributed-temperature sensing using stimulated Brillouin scattering in optical silica fibers,” Opt. Lett. **15**(18), 1038–1040 (1990). [CrossRef] [PubMed]

9. R. Bernini, A. Minardo, and L. Zeni, “Dynamic strain measurement in optical fibers by stimulated Brillouin scattering,” Opt. Lett. **34**(17), 2613–2615 (2009). [CrossRef] [PubMed]

10. X. Bao, C. Zhang, W. Li, M. Eisa, S. El-Gamal, and B. Benmokrane, “Monitoring the distributed impact wave on a concrete slab due to the traffic based on polarization dependence on stimulated Brillouin scattering,” Smart Mater. Struct. **17**(1), 015003 (2008). [CrossRef]

11. P. Chaube, B. G. Colpitts, D. Jagannathan, and A. W. Brown, “Distributed Fiber-Optic Sensor for Dynamic Strain Measurement,” IEEE Sens. J. **8**(7), 1067–1072 (2008). [CrossRef]

12. K. Y. Song and K. Hotate, “Distributed Fiber Strain Sensor With 1-kHz Sampling Rate Based on Brillouin Optical Correlation Domain Analysis,” IEEE Photon. Technol. Lett. **19**(23), 1928–1930 (2007). [CrossRef]

## 2. Theory

*E*and

_{p,s}(z,t)*E*in time

_{a}(z,t)*t*and position

*z*along the fiber [13]. Brillouin sensors focus on measuring the backscattered light, which gives information about changes in temperature or strain in the fiber. Considering pump pulses larger than the phonon life time, no pump depletion and not negative intensity, the Brillouin backscattered light power

*P*detected at the receiver can be given by:where

_{B}(z,ν)*P(t)*is the total power of the launched pulsed light,

*α*is the attenuation coefficient of the fiber,

*n*is the refractive index,

*c*is the velocity of light in vacuum and

*g*is the Brillouin gain spectrum (BGS); in this case

_{B}*g*has a Lorentzian shape and is assumed not to depend on

_{B}*z*. The parameter

*ν*is the frequency at which

_{B}*g*has a peak value

_{B}*g*, and

_{0}*Δν*is the full width at half-maximum (FWHM). The Brillouin frequency shift (BFS) has a linear dependence on the applied strain ε and the temperature variation

_{B}*ΔT*(at reference values of temperature

*T*and strain

_{0}*ε*) [7

_{0}7. T. Kurashima, T. Horiguchi, and M. Tateda, “Distributed-temperature sensing using stimulated Brillouin scattering in optical silica fibers,” Opt. Lett. **15**(18), 1038–1040 (1990). [CrossRef] [PubMed]

*ν*.

_{B}14. C. Galindez, F. J. Madruga, and J. M. Lopez-Higuera, “Brillouin frequency shift of standard optical fibers set in water vapor medium,” Opt. Lett. **35**(1), 28–30 (2010). [CrossRef] [PubMed]

*Δz*); thus the distance

*z*is given by

*JΔz*(with

*J*as the total number of points on the trace). Since the backscattered signal is weak, each trace of position versus Brillouin gain has to be obtained from an averaging process of

*N*samples. A high value of

*N*(~2000) improves the signal to noise ratio (SNR) of the data allowing a more accurate measurement. Each trace is obtained in a time interval

*Δt*(

*Δt ≈Nδt*); hence the measurement time

*T≈MΔt*can be defined by

*Δt*and the number of frequencies evaluated

*M*(

*m = 1, 2,…M,*being

*M*the ratio between span and step frequency). Hence, the backscattered light power at each point

*j*at frequency

*ν*can be computed as:

*N*number could be decreased, but the accuracy on the measurement can be negatively affected. To overcome this lack, a special algorithm capable of detecting small events among the data (no matter how small the

*N*number is) can be used, since dynamic events can be seen as temporally anomalies in the natural Brillouin gain. If the measurement is focused around the Brillouin peak gain, the required time is drastically reduced and dynamic events can be properly measured. Furthermore, it is possible to move the frequency of the probe according to a specific value, making only two or three measurements of frequency in a trace.

15. I. S. Reed and X. Yu, “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution,” IEEE Trans. Acoust. Speech Signal Process. **38**(10), 1760–1770 (1990). [CrossRef]

15. I. S. Reed and X. Yu, “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution,” IEEE Trans. Acoust. Speech Signal Process. **38**(10), 1760–1770 (1990). [CrossRef]

**and**

*H*_{0}**. The first hypothesis models the background as a Gaussian distribution with zero mean and an unknown background covariance matrix, which is estimated locally or globally from the data. The second hypothesis models the target as a linear combination of a target signature and background noise. So, under**

*H*_{1}**a spectral vector is represented by a Gaussian distribution with a mean equal to the signature of the target and a covariance matrix equal to the background covariance matrix in hypothesis**

*H*_{1}**. Let each signal data consisting of**

*H*_{0}*J*points represented by the column vector

**x**

*=*

_{i}*I;*then,

**is an**

*X**I*×

*J*data matrix. The two competing hypotheses for the RX-algorithm are given by:where

*a = 0*under

**and**

*H*_{0}*a >0*under

**respectively. The vector**

*H*_{1}**j**represents the noise and

**s**is the spectral signature of the signal (target). The target signature

**s**and the background covariance

*C*are assumed to be unknown. The model assumes that the data arise from two normal probability density functions with the same covariance matrix but different means. The background data

_{i}**is modeled as**

*H*_{0}*J*(0,

*C*) and the data with the target present

_{i}**is modeled as**

*H*_{1}*J*(

*s*,

*C*). In general, the hypothesis

_{i}**would have different covariance structure from**

*H*_{1}**, which should include the covariance of target magnitude. However, since the statistical structure of the anomaly**

*H*_{0}*a*cannot be defined in the RX-algorithm, the same covariance matrix for anomaly and background is adopted. Then the RX-algorithm can be written as:where

*I*traces of dimensionality

*M*(

*i = 1…I; m = 1…M*) of the backscattered light power at the Brillouin frequency peak (

*M*is the number of evaluated frequencies around the peak value); the data are obtained each time interval

*Δt*(time spent for measuring a trace). Since the RX-algorithm can detect variation from the clutter Brillouin data, the number

*N*in this technique can be decreased in relation to the

*N*used in traditional BOTDA systems. Hence, the power Brillouin gain on the fiber for

*I*traces can be written by

*I*×

*J*matrix). In the case of using only the Brillouin frequency peak

*M = m = 1*and

*ν = ν*.

_{B}

*RX**) or time (*

_{p}

*RX**) by:with*

_{t}## 3. Experimental results

*M*has to be defined. For simplicity, the value

*M*is set to 1, i.e.

*ν = ν*. So, in this case the event detection is focused on the exponential decaying behavior of the Brillouin gain around its maximum. Figure 3 shows the results of RX-algorithm at

_{B}*ν = ν*for a fiber section linearly strained that is placed at 2783.8 m, and the BGS of the same section without strain and a pumped light of 10ns (line dot in Fig. 3).

_{B}*N*is equal to 1000 and

*Δt*is 4 seconds. By making a simple skimming of Fig. 4.a, the only highlighted section from data is the third one, as well as the temporally behavior of this section. The classical measurements of BFS are obtained by completing a frequency swept. If the swept has a span of 360 MHz and step of 1 MHz, it takes 7.6 minutes (including the time spent by the electrical system and the acquisition card). Hence, the strain events have to take at least 430 seconds (

*T*= 430 s).

*ν*[8] (because the system is in Stokes gain regime) is used to compare with. It consists on measuring the minimum value at each position point from the vector

_{B}*N*to obtain reliable results using RX-algorithm is investigated. If

*N*is large, the noise of the measured trace can be reduced, but the time interval

*Δt*is increased;

*N*also depends on the spatial resolution and the measurement distance, i.e. the Stokes signal coming from a larger distance of 8 Km is weaker than a Stokes that comes from hundred meters of distance on the same fiber. To better explain the above idea, an optical fiber of 3000 m, a section of 1 m is stressed twice by a step motor 800 με during 20 s each 40 s. Results are summarized in Fig. 6 .

*N*. The evolution of segment in the fiber is cleaned up and the dynamic event is highlighted from the clutter Brillouin intensity. This is a measurement on how this dynamic event can be spatially detected, maintaining constant the event duration and the strain value. It shows a SNR >10 dB for

*N*= 200, significant if it is considered that

*Δt*is reduced from 6.83 s at

*N*= 1000 to 4.26 s at

*N*= 200.

*N*= 1000. In Fig. 7.a and 7.e the fiber section is strained 1500 με, in Fig. 7.b and 7.f the sections are strained 1200 με and 1500 με, in Fig. 7.c and 7.g the sections are strained 1200 με, 225 με and 1500 με, and in Fig. 7.d and 7.h the sections are strained 1500 με, 900 με, 300 and 500 με. In the second part of the figure (Fig. 7.e to h) the detection of the defects is made with

*N*= 200 and compared to the minimum intensity losses. Figure 8 shows that the technique can also be used for measuring one dynamic event, which has different values of duration on time (Fig. 8.a) and when it is periodic along the time (Fig. 8.b).

*ν*, whereas it is highlighted by using the RX-algorithm.

_{B}## 4. Discussion and Conclusions

## Acknowledgments

## References and links

1. | C. Galindez and J. M. Lopez-Higuera, “Decimeter spatial resolution by using differential pre-excitation BOTDA pulse technique,” IEEE Sens. J. |

2. | A. Minardo, R. Bernini, and L. Zeni, “Stimulated Brillouin scattering modeling for high-resolution, time-domain distributed sensing,” Opt. Express |

3. | K. Y. Song, Z. He, and K. Hotate, “Distributed strain measurement with millimeter-order spatial resolution based on Brillouin optical correlation domain analysis,” Opt. Lett. |

4. | M. A. Soto, G. Bolognini, and F. Di Pasquale, “Long-range simplex-coded BOTDA sensor over 120 km distance employing optical preamplification,” Opt. Lett. |

5. | A. Zornoza, A. Minardo, R. Bernini, A. Loayssa, and L. Zeni, “Pulsing the probe wave to reduce nonlocal effects in Brillouin optical time-domain analysis sensors,” IEEE Sens. J. |

6. | T. Horiguchi, T. Kurashima, and M. Tateda, “Technique to measure distributed strain in optical fibers,” IEEE Photon. Technol. Lett. |

7. | T. Kurashima, T. Horiguchi, and M. Tateda, “Distributed-temperature sensing using stimulated Brillouin scattering in optical silica fibers,” Opt. Lett. |

8. | Z. Liu, G. Ferrier, X. Bao, X. Zeng, Q. Yu, and A. Kim, “Brillouin Scattering Based Distributed Fiber Optic Temperature Sensing for Fire Detection,” in Proceedings of The 7th International Symposium on Fire Safety Conference (Worcester, 2002). |

9. | R. Bernini, A. Minardo, and L. Zeni, “Dynamic strain measurement in optical fibers by stimulated Brillouin scattering,” Opt. Lett. |

10. | X. Bao, C. Zhang, W. Li, M. Eisa, S. El-Gamal, and B. Benmokrane, “Monitoring the distributed impact wave on a concrete slab due to the traffic based on polarization dependence on stimulated Brillouin scattering,” Smart Mater. Struct. |

11. | P. Chaube, B. G. Colpitts, D. Jagannathan, and A. W. Brown, “Distributed Fiber-Optic Sensor for Dynamic Strain Measurement,” IEEE Sens. J. |

12. | K. Y. Song and K. Hotate, “Distributed Fiber Strain Sensor With 1-kHz Sampling Rate Based on Brillouin Optical Correlation Domain Analysis,” IEEE Photon. Technol. Lett. |

13. | R. W. Boyd, Non linear Optics (Academic Press; Elsevier Science, 2003). |

14. | C. Galindez, F. J. Madruga, and J. M. Lopez-Higuera, “Brillouin frequency shift of standard optical fibers set in water vapor medium,” Opt. Lett. |

15. | I. S. Reed and X. Yu, “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution,” IEEE Trans. Acoust. Speech Signal Process. |

**OCIS Codes**

(060.2370) Fiber optics and optical communications : Fiber optics sensors

(190.4370) Nonlinear optics : Nonlinear optics, fibers

(290.5900) Scattering : Scattering, stimulated Brillouin

**ToC Category:**

Sensors

**History**

Original Manuscript: June 8, 2011

Revised Manuscript: July 20, 2011

Manuscript Accepted: July 26, 2011

Published: September 14, 2011

**Citation**

Carlos A. Galindez, Francisco J. Madruga, and Jose M. Lopez-Higuera, "Efficient dynamic events discrimination technique for fiber distributed Brillouin sensors," Opt. Express **19**, 18917-18926 (2011)

http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-19-20-18917

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### References

- C. Galindez and J. M. Lopez-Higuera, “Decimeter spatial resolution by using differential pre-excitation BOTDA pulse technique,” IEEE Sens. J.PP(99), 1–1 (2011). [CrossRef] [PubMed]
- A. Minardo, R. Bernini, and L. Zeni, “Stimulated Brillouin scattering modeling for high-resolution, time-domain distributed sensing,” Opt. Express15(16), 10397–10407 (2007). [CrossRef] [PubMed]
- K. Y. Song, Z. He, and K. Hotate, “Distributed strain measurement with millimeter-order spatial resolution based on Brillouin optical correlation domain analysis,” Opt. Lett.31(17), 2526–2528 (2006). [CrossRef] [PubMed]
- M. A. Soto, G. Bolognini, and F. Di Pasquale, “Long-range simplex-coded BOTDA sensor over 120 km distance employing optical preamplification,” Opt. Lett.36(2), 232–234 (2011). [CrossRef] [PubMed]
- A. Zornoza, A. Minardo, R. Bernini, A. Loayssa, and L. Zeni, “Pulsing the probe wave to reduce nonlocal effects in Brillouin optical time-domain analysis sensors,” IEEE Sens. J.11, 1067–1068 (2011). [CrossRef]
- T. Horiguchi, T. Kurashima, and M. Tateda, “Technique to measure distributed strain in optical fibers,” IEEE Photon. Technol. Lett.2(5), 352–354 (1990). [CrossRef]
- T. Kurashima, T. Horiguchi, and M. Tateda, “Distributed-temperature sensing using stimulated Brillouin scattering in optical silica fibers,” Opt. Lett.15(18), 1038–1040 (1990). [CrossRef] [PubMed]
- Z. Liu, G. Ferrier, X. Bao, X. Zeng, Q. Yu, and A. Kim, “Brillouin Scattering Based Distributed Fiber Optic Temperature Sensing for Fire Detection,” in Proceedings of The 7th International Symposium on Fire Safety Conference (Worcester, 2002).
- R. Bernini, A. Minardo, and L. Zeni, “Dynamic strain measurement in optical fibers by stimulated Brillouin scattering,” Opt. Lett.34(17), 2613–2615 (2009). [CrossRef] [PubMed]
- X. Bao, C. Zhang, W. Li, M. Eisa, S. El-Gamal, and B. Benmokrane, “Monitoring the distributed impact wave on a concrete slab due to the traffic based on polarization dependence on stimulated Brillouin scattering,” Smart Mater. Struct.17(1), 015003 (2008). [CrossRef]
- P. Chaube, B. G. Colpitts, D. Jagannathan, and A. W. Brown, “Distributed Fiber-Optic Sensor for Dynamic Strain Measurement,” IEEE Sens. J.8(7), 1067–1072 (2008). [CrossRef]
- K. Y. Song and K. Hotate, “Distributed Fiber Strain Sensor With 1-kHz Sampling Rate Based on Brillouin Optical Correlation Domain Analysis,” IEEE Photon. Technol. Lett.19(23), 1928–1930 (2007). [CrossRef]
- R. W. Boyd, Non linear Optics (Academic Press; Elsevier Science, 2003).
- C. Galindez, F. J. Madruga, and J. M. Lopez-Higuera, “Brillouin frequency shift of standard optical fibers set in water vapor medium,” Opt. Lett.35(1), 28–30 (2010). [CrossRef] [PubMed]
- I. S. Reed and X. Yu, “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution,” IEEE Trans. Acoust. Speech Signal Process.38(10), 1760–1770 (1990). [CrossRef]

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