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Developing automatic algorithmic tools for targets' detection and classification in a fiber-optic Distributed Acoustic Sensing (DAS) system is a challenging task. The main hurdle is the need to produce a large-scale dataset of tagged events to facilitate the training of the algorithms. This task requires considerable resources in terms of manpower, computing time and computer memory. In contrast, generating a training dataset via a computer simulation can significantly simplify the development stage and allow tremendous saving in time and costs. This approach, however, requires highly accurate modeling of the optical DAS system, the generation and propagation of the seismic/acoustic waves in the medium and the interaction between the waves to the fiber. The physical parameters and details needed for such modeling are rarely available. In this paper, a novel approach for efficient generation of training data is introduced and demonstrated. It is based on using Generative Adversarial Network (GAN) to transform simulation data to accurately mimic genuine data based on a relatively small experimental database labeled manually. The new approach is verified with experimental data taken from a 5km long DAS sensor yielding 94% classification accuracy between ambient noise and human steps at the vicinity of the buried fiber.

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Rayleigh scattering-based dynamic strain sensing with high spatial resolution, fast update rate and high sensitivity is highly desired for applications such as structural health monitoring and shape sensing. A key issue in dynamic strain sensing is the trade-off between spatial resolution and the Signal to Noise Ratio (SNR). This trade-off can be greatly relaxed with the use of coding. A sequence of optical pulses is injected into the fiber and the detected backscattered signal is cross-correlated with the original signal. With the use of coding, SNR is indeed improved, but if the sequence is not well chosen, the resulting peak to sidelobe ratio (PSR) can be rather low. An excellent choice of codes are biphase Legendre sequences which offer near perfect periodic autocorrelation (PPA). Other common issues in Rayleigh scattering-based sensing techniques are signal fading and dynamic range. The former issue can occur due to destructive interference between lightwaves that are scattered from the same spatial resolution cell and, in coherent detection schemes, when the polarization states of the backscattered light and the reference light are mismatched. The latter issue is a concern in phase sensitive schemes which require signal jumps not to exceed 2π. In this paper, a biphase Legendre sequence with 6211 pulses is used in conjunction with polarization diversity scheme and a PM fiber. The setup provides two independent measurements of the sensing fiber complex profile and achieves highly sensitive, distributed dynamic strain sensing with very low probability of fading. In addition, the system can handle both very large perturbation signals and very small perturbation signals. The system operated at a scan rate of ∼ 107kHz and achieved spatial resolution of ∼10cm and sensitivity of ∼1.1 mrad/ √ Hz. The ratio between the powers of the maximum and minimum excitations that can be measured by the system is 136 dB.

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Recently it was shown that sinusoidal frequency scan optical frequency domain reflectometry (SFS-OFDR) can achieve remarkable performance in applications of distributed acoustic sensing (DAS). The main advantage of SFS-OFDR is the simplicity with which highly accurate sinusoidal frequency scans can be generated (in comparison with linear frequency scans). One drawback of SFS-OFDR has been the computationally intensive algorithm it required for processing of the measured backscatter data. The complexity of this algorithm was O(N) where N is the number of backscatter samples. In this work a fast processing algorithm for SFS-OFDR, with computational complexity O (N log N), is derived and its performance and limitations are studied in details. The new algorithm facilitated highly sensitive DAS operation over a sensing fiber of 64km, with 6.5m resolution and scan rate of 400Hz. The high sensitivity of the system was demonstrated in a field trial where it successfully detected human footsteps near the end of the fiber with excellent SNR.

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Distributed acoustic sensing has been traditionally implemented using optical reflectometry. Here we describe an alternative to the common interrogation approaches. According to the new method the frequency of the source is varied sinusoidally with time. For a sufficiently high scan frequency there is a position along the fiber,  z(0), for which the roundtrip time is half the scan period. Back-reflections from this point will generate a linear chirp at the receiver output. The Fractional Fourier Transform (FrFT) is used to analyze the receiver output and yields the reflection profile at z(0) and its vicinity. The method, which enables high spatial resolution at long distances with high scan rates, is demonstrated by detecting deliberate perturbations in the Rayleigh backscatter profile at the end of a 20km fiber with a scanning frequency of ~2.5kHz. The spatial resolution at this range and scan-rate is characterized by a measurement of the backscatter profile from a FBG's-array and is found to be ~2.8m.