Optical Time Domain Reflectometer Research Articles

Degradation Analysis of Single-Mode and Multimode Fibers in a Full-Scale Wellbore and Its Impact on DAS and DTS Measurements

Distributed fiber optic sensors (DFOS) are viewed as a promising technology for efficient, cost-effective, and non-intrusive real-time monitoring for a variety of applications. The chemical passivity, non-magnetic interference behavior, and ability to acquire continuous measurements at high-spatial resolution have made DFOS very attractive for in-situ measurement of temperature, strain, vibration, etc. The accuracy of the measured parameter by the DFOS is highly dependent on the strength and quality of the optical signal returning from the point of interest. The signal degradation in DFOS results from optical losses due to attenuation, absorption, scattering, bending, dispersion, and coupling. This study analyzes the degradation observed in both the single-mode and multimode fibers installed in a 1574 mdeep wellbore for acquiring distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) data, respectively. The degradation was unexpected as it was observed within three years of DFOS installation in an experimental, non-producing well that contains non-corrosive fluids and operates in a low-temperature (<54.4 °C) and low-pressure (<3500 psi or 24131 kPa) environment. A root-cause analysis was performed to investigate the cause of this degradation using time-lapse optical loss measurements acquired at different wavelengths using an optical time-domain reflectometer. Degradation of the multimode fiber was also examined by the time-lapse analysis of the Raman backscatter that is used for DTS measurement. Based on the investigation, microbending was found to be the main cause of the observed degradation. Its impact on the quality of the DAS and DTS measurements was also analyzed both qualitatively and quantitively. Recommendations for minimizing degradation are presented.

SNR Enhancement for BOTDR With Spatial-Adaptive Image Denoising Method

A spatial-adaptive image denoising (SAID) method is proposed to enhance the signal-to-noise ratio (SNR) of Brillouin optical time domain reflectometer (BOTDR) without deterioration of spatial resolution. Through analyzing the principle of denoising, the spatial-adaptive denoising network structure is constructed, which can effectively reduce noise and avoid over-smoothing. Moreover, a high-similarity dataset generation method is designed to further enhance the denoising effect. The SAID method is experimentally implemented on two-dimension Brillouin gain spectrum matrix over a 25.1 km-long optical fiber with the spatial resolution of 2 m. The experimental results show that the SNR enhancement of SAID method is 21.92 dB, and the data processing time is only 2.48 s. Whereas the SNR enhancement of conventional 10000 times trace averaging method is merely 9.98 dB, but the data processing time is increased to 174.52 s. Therefore, it is demonstrated that the SAID method has obvious advantages in SNR enhancement and data processing speed without deteriorating the spatial resolution of BOTDR.

Using Phase-Sensitive Optical Time Domain Reflectometers to Develop an Alignment-Free End-to-End Multitarget Recognition Model

This pattern recognition method can effectively identify vibration signals collected by a phase-sensitive optical time-domain reflectometer (Φ-OTDR) and improve the accuracy of alarms. An alignment-free end-to-end multi-vibration event detection method based on Φ-OTDR is proposed, effectively detecting different vibration events in different frequency bands. The pulse accumulation and pulse cancellers determine the location of vibration events. The local differential detection method demodulates the vibration event time-domain variation signals. After the extraction of the signal time-frequency features by sliding window, the convolution neural network (CNN) further extracts the signal features. It analyzes the temporal relationship of each group of signal features using a bidirectional long short-term memory network (Bi-LSTM). Finally, the connectionist temporal classification (CTC) is used to label the unsegmented sequence data to achieve single detection of multiple vibration targets. Experiments show that using this method to process the collected 8563 data, containing 5 different frequency bands of multi-vibration acoustic sensing signal, the system F1 score is 99.49% with a single detection time of 2.2 ms. The highest frequency response is 1 kHz. It is available to quickly and efficiently identify multiple vibration signals when a single demodulated acoustic sensing signal contains multiple vibration events.

190km Φ-OTDR with bidirectional Raman and relay erbium-doped fiber hybrid amplification

In this paper, a long-distance coherent detection phase-sensitive optical time-domain reflectometer (Φ-OTDR) scheme with bidirectional Raman and relay erbium-doped fiber hybrid amplification is proposed. The bidirectional Raman pump light acting on the erbium-doped fiber (EDF) can realize the passive amplification of pulsed signal light and Rayleigh backward scattering (RBS) light at the same time. The scheme utilizes bidirectional 1455 nm pumps as the pump source of relay amplification to achieve a longer sensing distance. Experimental results demonstrate that, an ultra-long Φ-OTDR system with 190 km sensing distance and 22 m spatial resolution is realized. Multi-point vibration signals are located and restored at 69.67 km, 120.13 km and 190.79 km, respectively. The average signal-to-noise ratio (SNR) of positioning curve is 10.32 dB, the average SNR of demodulated phase signal is 26.02 dB, and the frequency response range is from 5 Hz to 150 Hz. Furthermore, the sensing system has good linear characteristics of phase demodulation with a correlation coefficient of 0.997. The actual disturbance events are also detected at the far-end of sensing fiber. To the best of our knowledge, this system achieves the longest-distance Φ-OTDR without relay power supply. This passive hybrid amplification method can avoid the intermediate power supply along sensing fiber, which provides a reference for long-distance vibration monitoring of Φ-OTDR system.

The Sensitivity Investigation of Fiber Optic Paths in the Framework of an Intruder Localization at a Protected Facility

The paper considers the possibility of using communication lines already existing at urban development sites based on such information transfer technologies as «fiber-to-the-office» and «fiber-to-the-desk» in relation to the tasks of physical protection of objects. The aspects of using distributed acoustic sensors based on a phase-sensitive optical time-domain reflectometer for localizing sources of acoustic impact in real-time, that is, for determining the location of an intruder on a protected object, are considered. The sensitivity of optical paths to acoustic influences corresponding to the speech signals of the alleged intruder was assessed. An optical path based on optical fiber in an optical module with a hydrophobic filling is considered. An analysis of the spectral sensitivity of the optical fiber samples under study has been carried out. An assessment of the influence of the conditions for the passage of the route of laying the optical cable and the interaction of the acoustic sensor with the surrounding objects was carried out. The analysis of the results obtained during the test events at the experimental site was carried out.

Intensity and phase stacked analysis of a Φ-OTDR system using deep transfer learning and recurrent neural networks.

Distributed acoustic sensors (DAS) are effective apparatuses that are widely used in many application areas for recording signals of various events with very high spatial resolution along optical fibers. To properly detect and recognize the recorded events, advanced signal processing algorithms with high computational demands are crucial. Convolutional neural networks (CNNs) are highly capable tools to extract spatial information and are suitable for event recognition applications in DAS. Long short-term memory (LSTM) is an effective instrument to process sequential data. In this study, a two-stage feature extraction methodology that combines the capabilities of these neural network architectures with transfer learning is proposed to classify vibrations applied to an optical fiber by a piezoelectric transducer. First, the differential amplitude and phase information is extracted from the phase-sensitive optical time domain reflectometer (Φ-OTDR) recordings and stored in a spatiotemporal data matrix. Then, a state-of-the-art pre-trained CNN without dense layers is used as a feature extractor in the first stage. In the second stage, LSTMs are used to further analyze the features extracted by the CNN. Finally, a dense layer is used to classify the extracted features. To observe the effect of different CNN architectures, the proposed model is tested with five state-of-the-art pre-trained models (VGG-16, ResNet-50, DenseNet-121, MobileNet, and Inception-v3). The results show that using the VGG-16 architecture in the proposed framework manages to obtain a 100% classification accuracy in 50 trainings and got the best results on the Φ-OTDR dataset. The results of this study indicate that pre-trained CNNs combined with LSTM are very suitable to analyze differential amplitude and phase information represented in a spatiotemporal data matrix, which is promising for event recognition operations in DAS applications.

Phase-sensitive optical time domain reflectometry based on geometric phase measurement

A phase-sensitive optical time domain reflectometer based on coherent heterodyne detection of geometric phase in the beat signal of light, is reported for the first time to our knowledge. The use of the geometric phase to extract strain makes it immune to polarisation diversity fading. This is because a polarisation mismatch between the interfering beams is not a hindrance to its measurement. The geometric phase is calculated using the amplitude of the beat signal and individual beam intensities without any need for phase unwrapping. It is measured per beat period and can be equated with the traditionally measured dynamic phase with appropriate scaling. The results show that the system based on the geometric phase successfully measures strain, free from polarisation mismatch fading and phase unwrapping errors, providing a completely novel solution to these problems.

Accelerating the phase demodulation process for heterodyne Φ-OTDR using spatial phase shifting.

An effective orthogonal signal generation method for heterodyne-detection-based phase-sensitive optical time-domain reflectometer systems is proposed to accelerate the phase demodulation process. The demodulation principle is based on the spatial phase shifting technique. By exploiting the relative phase difference between adjacent spatial sampling channels, the orthogonal signal is easily obtained from basic algebra calculations. The simulation and experimental results showed that the proposed method achieved >100% computation speed improvement compared with the conventional methods, with a slight trade-off in phase demodulation performance. Therefore, the proposed method is potentially beneficial for the distributed acoustic sensing technology for reducing the computation complexity of phase demodulation procedures.

Distributed Acoustic Sensing Based on Microtremor Survey Method for Near-Surface Active Faults Exploration: A Case Study in Datong Basin, China.

Active fault detection has an important significance for seismic disaster prevention and mitigation in urban areas. The high-density station arrays have the potential to provide a microtremor survey solution for shallow seismic investigations. However, the resolution limitation of the nodal seismometer and small-scale lateral velocity being inhomogeneous hinder their application in near-surface active fault exploration. Distributed acoustic sensing (DAS) has been developed rapidly in the past few years; it takes an optical fiber as the sensing medium and signal transmission medium, which can continuously detect vibration over long distances with high spatial resolution and low cost. This paper tried to address the issue of near-surface active fault exploration by using DAS. We selected a normal fault in the southern Datong basin, a graben basin in the Shanxi rift system in north China, to carry out the research. Microtremor surveys across the possible range of the active fault were conducted using DAS and nodal seismometers, so as to obtain a shallow shear wave velocity model. Meanwhile, we applied a Brillouin optical time domain reflectometer (BOTDR) and distributed temperature sensing (DTS) to monitor the real-time fluctuation of ground temperature and strain. Our results show that the resolution of the deep structures of the fault via the microtremor survey based on DAS is lower than that via the seismic reflection; whereas, their fault location is consistent, and the near-surface structure of the fault can be traced in the DAS results. In addition, both the BOTDR and DTS results indicate an apparent consistent change in ground temperature and strain across the fault determined by the DAS result, and the combination of surface monitoring and underground exploration will help to accurately avoid active faults and seismic potential assessment in urban areas.

Distributed Brillouin optical fiber sensors assisted by first‐order Raman amplification with window functions

Raman amplification can extend the sensing distance of distributed Brillouin optical fiber sensor, and there is a corresponding relationship between the optimal Raman pump, the sensing distance, and the lanched probe power. We propose a scheme of co-directional Raman pumping Brillouin optical time-domain reflectometer (BOTDR) with window functions to make a better tradeoff between sensing distance and signal-to-noise ratio (SNR). The mathematical model of window functions is designed based on the relationship between the sensing distance and Raman pump power. Both simulations and experiments analyze the distributions of Brillouin scattering power, which are detected under the fixed Raman pump and processed by the window functions. We then obtain the Brillouin frequency shift (BFS) distribution curve by Lorentzian curve fitting. The experimental results show that the system SNR is increased by 3.3 dB at the near-end of the 78.8 km long sensing fiber. Moreover, the accuracy of temperature and strain at the far-end is enhanced by 2.75 °C and 389 με by using window functions, respectively.

Identification of external media using a long-period grating and optical time-domain reflectometry.

The application of signal processing and feature extraction to reflectometry traces aiming at sensor interrogation is not sufficiently explored. In this work, traces produced by an optical time-domain reflectometer in experiments using a long-period grating in different external media are analyzed using signal processing techniques inspired by audio processing. The objective is to demonstrate that, using this type of analysis, it is possible to correctly identify the external medium through the characteristics of the reflectometry trace. Results show that the features extracted from the traces were able to produce good classifiers, one of them achieving 100% correct classification for the data set presently considered. This technology could be applied in scenarios where it is necessary to distinguish among a given set of gases or liquids nondestructively.

An open dataset of φ-OTDR events with two classification models as baselines

Event classification with machine learning methods, has now become the research spotlight for phase sensitive optical time domain reflectometer (Ф-OTDR, also referred to as DAS or DVS) system. Unlike in the computer science field, however, each team works on its own dataset and their codes in private, which makes it difficult to compare and evaluate different models on a fair basis and impairs the value of those work. Hence, we set up an intensity-based Ф-OTDR system, from which we collect data, build and make public the first open dataset, according to our searches via google. It contains six types of events with a total of 15,612 samples. We also publicize codes for two common baseline models, which are the SVM (support vector machine, 1D method) and CNN (convolutional neural network, 2D approach) models. The average recognition rates are greater than 82 % for SVM and 94 % for CNN. The dataset and codes can be used as data source and baselines for Ф-OTDR event classification, providing fair evaluation criteria and for further development of machine learning models.

Phase Noise Suppression for φ-OTDR With Optimized Minima Controlled Recursive Averaging Method

We propose a phase noise suppression method for phase-sensitive optical time-domain reflectometer ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula> -OTDR) system. The signal-to-noise ratio (SNR) of the demodulated phase is improved by the optimized minima controlled recursive averaging (MCRA) method. First, the original phase waveform is divided into frames. Then, the minimum control is used to track the minimum power spectrum of phase noise with Gaussian distribution. The prior variance of noise is corrected by recursive averaging. The attenuation factor is determined by appropriate threshold parameters and the noise prior variance. Ultimately, the phase waveform is reconstructed in the time–frequency domain. In this way, the SNR of the reconstructed waveform is intensified remarkably. Experimental results show that the SNR of the demodulated phase is improved by 9.12–19.63 dB in the range of 20–1200 Hz. Furthermore, the enhancement of acoustic extracted from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula> -OTDR could also be verified experimentally with the proposed approach.

A wavelength scanning BOTDR sensing system based on Rayleigh and Brillouin self-heterodyne detection

We propose a wavelength scanning Brillouin Optical Time Domain Reflectometer (WS-BOTDR) sensing system based on Rayleigh and Brillouin (RB) self-heterodyne detection for high-accuracy temperature measurement. This method scans the power spectrum by adjusting the wavelength of laser rather than the frequency, which possesses the benefits of both electric frequency difference scanning and optical frequency difference scanning. Furthermore, the adverse effects of laser wavelength instability, polarization fading and phase fluctuation can be effectively eliminated by employing RB self-heterodyne detection. The experimental results demonstrate that the proposed method can achieve a high-accuracy measurement of 0.8 °C in the 50 m heating section of 9.5 km optical fiber end. It is also found that, from a practical point of view, the proposed method has prospective applications where both high-accuracy measurement and long-distance sensing are the primary concerns.

Selective distributed optical fiber sensing system based on silicone cladding optical fiber and Rayleigh backscattering reflectometry for the detection of hydrocarbon leakages

A new system that exploits optical fiber distributed sensing for the detection of hydrocarbons is presented. The system relies on a custom-designed silica core/thin silicone cladding optical fiber to provide a quick and selective absorption of oil products through the cladding; moreover, it exhibits insensitivity to water. Optical fiber sensor interrogation is performed by detecting Rayleigh backscattering using an optical time domain reflectometer or, in a high-resolution version, an optical frequency domain reflectometer. The Rayleigh backscattering signal is influenced by a change in the refractive index of the cladding, which locally modifies the guiding properties of the fiber; and by the swelling of the cladding, resulting from hydrocarbon diffusion, that produces local stress and results in an associated increased reflection. The system has been tested with different solvents and has exhibited selectivity and a rapid response to exposure to high refractive index hydrocarbons, with the sensing fiber yielding a response time of the order of 1 s. The system, in its high-resolution version, is able to accurately locate leakages with an accuracy of 14 cm.

Automated Damage Detection Using Lamb Wave-Based Phase-Sensitive OTDR and Support Vector Machines.

In this paper, we propose and demonstrate a damage detection technique based on the automatic classification of the Lamb wave signals acquired on a metallic plate. In the reported experiments, Lamb waves are excited in an aluminum plate through a piezoelectric transducer glued onto the monitored structure. The response of the monitored structure is detected through a high-resolution phase-sensitive optical time-domain reflectometer (ϕ-OTDR). The presence and location of a small perturbation, induced by placing a lumped mass of 5 g on the plate, are determined by processing the optical fiber sensor data through support vector machine (SVM) classifiers trained with experimental data. The results show that the proposed method takes full advantage of the multipoint sensing nature of the ϕ-OTDR technology, resulting in accurate damage detection and localization.

Real-time classification for Φ-OTDR vibration events in the case of small sample size datasets

Efficient classification of vibration signals detected by phase-sensitive optical time domain reflectometer (Φ-OTDR) based on small samples is an effective method to reduce the false alarm rate without GPU or large data sets. This paper proposes a fiber optic system vibration event recognition method based on a combination of image segmentation pre-processing, texture, statistical, morphological feature extraction, and weighted support vector machine (WSVM), which can effectively classify-five types of vibration events in high-speed railway perimeter intrusion detection with small sample data and no parallel processing units. Erosion and dilation operations are applied to vibration signal image feature enhancement in image pre-processing. The vibration signal region and background are separated by the maximum inter-class variance method, then 35 features of the vibration signal region are calculated and finally employed to construct a WSVM. Experiments show that the method achieves 99 FPS and 98.8% accuracy on the test set with 330 vibration images as the training set to build the model without GPU and in the presence of interference signals. It provides a generalized Φ-OTDR vibration event recognition method for small samples.

Efficient Fault Detection Algorithm in Fiber Optic Communication Systems

Primary concern of optical communication system is the signal loss, which need to be traced precisely to enhance the efficiency of the system. Among numerous available techniques, optical time domain reflectometer (OTDR) is the most suitable one for characterizing and tracing the losses. It measures the fraction of a probe pulse that scatters back from the fiber optic cable. Due to the presence of small levels of backscattering in single-mode fiber at long wavelengths, very sensitive optical detector is necessary to achieve sufficient range performance. In the present research, a novel yet simple approach has been demonstrated to understand the range of optical fiber cable feasibility on fault detection and rectification technique. The actual place of fault is marked with the variation in the measured values of OTDR. Thus, it becomes a difficult task to trace and locate the actual place of fault detection, which leads to an inefficient rectification method. The observed values of OTDR shows a gradual decrease of accuracy in locating the actual place of fault. To resolve these issues, here we try to develop an algorithm which is able to easily and efficiently trace the exact location of fault on earth.

Concise method for high-precision vibration recovery of Φ-OTDR based on the GF-FastICA algorithm.

To efficiently restore the vibration signals of a phase-sensitive optical time domain reflectometer (Φ-OTDR), the GF-FastICA joint algorithm is proposed, which combines guided filtering with fast independent component analysis (FastICA). The marked region of vibration is precisely located by guided filtering. FastICA deals with the optimal phase mixing matrix of the marked region to separate the vibration signals from the noise-containing phase signals. The experimental results show that the GF-FastICA achieves a correlation coefficient of 0.998 for 5-Hz vibration signal recovery from a 14.3-km sensing fiber, verifying the potency of the algorithm. Compared with the traditional method and FastICA only, GF-FastICA improves the root mean square error (RMSE) metric by an order of magnitude, which is approaching an experience value of 10-3.

Single-photon detector-based long-distance Brillouin optical time domain reflectometry

We present a long-range Brillouin optical time domain reflectometer (BOTDR) based on photon counting technology. We demonstrate experimentally the ability to perform a distributed temperature measurement, by detecting a hot spot in a thermal bath at 100 km, and the possibility to achieve measurement until 120 km with a spatial resolution of 10 m. We use the slope of a fiber Bragg grating (FBG) as a frequency discriminator, to convert count rate variation into a frequency shift. A performance study of our distributed sensor as a function of spatial resolution is also presented.