Reflectometer System Research Articles

Sparse representation-based noise reduction for BOTDR monitoring signals in foundation pit anchor cables

Abstract Brillouin optical time-domain reflectometer (BOTDR) systems are commonly challenged by low signal-to-noise ratio (SNR) in foundation pit monitoring. This study proposes a noise-suppression method for BOTDR signals utilizing sparse representation (SR). The method involves creating an initial dictionary from the eigenvectors of the normalized graph Laplace matrix. The K-singular value decomposition and orthogonal matching pursuit algorithms are combined to update the dictionary and coefficient matrix, facilitating the SR of the signal’s intrinsic features and the removal of random noise. This results in improved quality of the reconstructed signal. An experimental system for BOTDR temperature sensing was developed to assess the algorithm’s denoising capabilities. The algorithm showed significant improvements in SNR and reductions in sample entropy (SE) compared to techniques such as wavelet threshold denoising, empirical wavelet transform, and empirical mode decomposition. Specifically, the average SNR increase was 27.4%, 15.4%, 13.1%, and 17.9%, while the average SE decrease was 24.4%, 16.0%, 15.4%, and 47.9% for the respective comparisons. The proposed Laplace-based dictionary also outperformed the discrete cosine transform dictionary. Field tests were conducted at the Beijing Stomatological Hospital, where the algorithm applied to on-site anchor cable fiber optic monitoring signals, achieving an average SE decrease of 45.2%. The research provides an effective denoising method for the application of BOTDR technology in foundation pit monitoring, underscoring the approach’s novelty and practical application.

An improved denoising method for [formula omitted]-OTDR signal based on the combination of temporal local GMCC and ICEEMDAN-WT

A noise reduction method based on the combination of improved complete ensemble empirical mode decomposition with adaptive noise and temporal local generalized maximum cross-correlation entropy and wavelet threshold (ICEEMDAN-TLGMCC-WT) is proposed to improve the noise rejection ratio (NRR) of phase-sensitive optical time-domain reflectometer (φ-OTDR) system signals. Firstly, the principle of the proposed ICEEMDAN-TLGMCC-WT denoising method is introduced. On this basis, experiments are carried out to verify the feasibility of the noise reduction algorithm for non-stationary signals. and the results demonstrate that the vibration event can be detected with an improved average NRR of 72.18 dB and a root mean square error (RMSE) 0.017 within 5 km sensing distance by employing the ICEEMDAN-TLGMCC-WT denoising method, which confirm the effectiveness of proposed scheme for the performance improvement of φ-OTDR system.

Potential multi-parametric OFDR system based on the wavelength-division cross-correlation method

In this study, an optical frequency domain reflectometer (OFDR) system for simultaneous measurement of temperature, strain, and relative humidity (RH) based on the wavelength-division cross-correlation method is proposed and experimentally demonstrated. The Rayleigh scattered spectrum shifts introduced by environmental changes can be demodulated by the cross-correlation operation, and the magnitude of the shifts exhibits variability in distinct wavelength subregions. Leveraging the distinct sensitivity characteristics of different parameters across different wavelength subregions, simultaneous multi-parameter decoupling can be achieved. In the signal processing flow, the frequency domain signals in different wavelength subregions are intercepted, and then the cross-correlation operation is performed separately to obtain the spectrum shift information in different wavelength subregions. The decoupling matrix is constructed to realize the decoupling of the three parameters based on the sensitivity differences of temperature, strain, and RH in different wavelength subregions. This novel and simple multi-parameter sensing system, to the best of our knowledge, has potential applications in engineering monitoring, chemical applications, and environmental measurements.

Design of a low frequency, density profile reflectometer system for the MAST-U spherical tokamak.

Validated and accurate edge profiles (temperature, density, etc.) are vitally important to the Mega Ampere Spherical Tokamak Upgrade (MAST-U) divertor and confinement effort. Density profile reflectometry has the potential to significantly add to the measurement capabilities currently available on MAST-U (e.g., Thomson scattering and Langmuir probes). This work presents the diagnostic requirements, problems, and solutions facing profile reflectometry in spherical tokamaks and MAST-U in particular. Requirements include density measurements near zero electron density in the scrape off layer region, coverage for a broad range of MAST-U plasma parameters, high time (≤10microseconds) and spatial resolutions (≤1cm), reliability, and identification of the plasma start frequency.

Phase unwrapping error identification and suppression method in φ-OTDR systems based on PELT-VMD-ARIMA.

In phase-sensitive optical time-domain reflectometer (φ-OTDR) systems, phase unwrapping errors can distort vibration information. To address this issue, a phase unwrapping error identification and suppression method combining pruned exact linear time (PELT) changepoint detection, variational mode decomposition (VMD), and autoregressive integrated moving average (ARIMA) models, termed PELT-VMD-ARIMA, is proposed. Firstly, the principle of the proposed method is introduced, and its effectiveness is verified through a series of numerical simulation experiments. Next, piezoelectric transducers (PZTs) are employed as seismic sources in experiments involving single-frequency and chirp signals. Compared to the mean-shift method, the proposed method reduces the average root mean square error (RMSE) by 70.36% within 2δ range around the changepoints. Finally, the proposed method was validated through an active source seismic application. The results demonstrate the efficacy of the proposed method in identifying and suppressing phase unwrapping errors, thereby enhancing signal quality. This method enhances the vibration recognition capability of φ-OTDR systems, which facilitates precise distributed acoustic sensing applications.

An SNR Enhancement Method for Φ-OTDR Vibration Signals Based on the PCA-VSS-NLMS Algorithm.

To improve the signal-to-noise ratio (SNR) of vibration signals in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, a principal component analysis variable step-size normalized least mean square (PCA-VSS-NLMS) denoising method was proposed in this study. First, the mathematical principle of the PCA-VSS-NLMS algorithm was constructed. This algorithm can adjust the input signal to achieve the best filter effect. Second, the effectiveness of the algorithm was verified via simulation, and the simulation results show that compared with the wavelet denoising (WD), Wiener filtering, variational mode decomposition (VMD), and variable step-size normalized least mean square (VSS-NLMS) algorithms, the PCA-VSS-NLMS algorithm can improve the SNR to 30.68 dB when the initial SNR is -1.23 dB. Finally, the PCA-VSS-NLMS algorithm was embedded into the built Φ-OTDR system, an 11.22 km fiber was measured, and PZT was added at 10.19-10.24 km to impose multiple sets of fixed-frequency disturbances. The experimental results show that the SNR of the vibration signal is 8.77 dB at 100 Hz and 0.07 s, and the SNR is improved to 26.17 dB after PCA-VSS-NLMS filtering; thus, the SNR is improved by 17.40 dB. This method can improve the SNR of the system's position information without the need to change the existing hardware conditions, and it provides a new scheme for the detection and recognition of long-distance vibration signals.

Interference fading suppression with fault-tolerant Kalman filter in phase-sensitive OTDR

A multi-sensor information fusion algorithm based on fault-tolerant Kalman filter is proposed in phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, for achieving fading-free distributed vibration sensing. Firstly, a fault-tolerant dual-core complementary array model is designed. The Rayleigh scattering signal denoising, and vibration existence judgment of localization points are carried out to obtain the differentiated frequency demodulation results of the sensing points of the dual-core fiber array. Then a fault-tolerant control strategy is used to determine the sensor weight coefficients and vibration judgment coefficients during data fusion processing, and array data fusion is carried out based on time series data using Kalman filter to realize error value identification and filling. The advantage of this method is the combination of redundant data in a complementary way to improve the system stability. The frequency response ranges from 10 Hz to 2400 Hz and the localization accuracy is 98.33%. The influence of key parameters on the frequency demodulation performance of fault-tolerant Kalman filter is discussed, and a standard deviation of 14.6 Hz and an average error of 7.6 Hz are obtained. The demodulation frequency data matrix obtained by the classical demodulation method has a demodulation error probability of 89.18%, which proves the widespread existence of demodulation errors in vibration signals. The fusion error of demodulation frequency is reduced to 0.25 Hz, the frequency demodulation accuracy reaches 100%, and the demodulation error caused by interference attenuation can be completely eliminated. This system based on fault-tolerant Kalman filter has the characteristics of simple multiplexing structure, interference fading resistance and stable demodulation performance.

Rayleigh and Brillouin self-heterodyne detection Brillouin optical time domain reflectometer system based on phase shift keying pulse encoding

Rayleigh and Brillouin self-heterodyne detection Brillouin optical time domain reflectometer system based on phase shift keying pulse encoding

Brillouin frequency shift extraction by bidirectional integration of Brillouin gain spectrum

A novel Brillouin frequency shift (BFS) extraction approach based on bidirectional integration is proposed and investigated for mitigating the fluctuations in Brillouin optical time domain reflectometer (BOTDR) system. The proposed method involves calculating the BFS through the forward and backward integrations of the original Brillouin gain spectrum (BGS) data. The findings from both simulations and experiments demonstrate that bidirectional integration achieves superior BFS accuracy compared to Lorentzian curve fitting. To further evaluate its performance, the root mean square error of BFS is measured under the different signal-to-noise ratios (SNRs) and frequency steps and the results show that bidirectional integration has higher tolerance for low SNR. Specifically, bidirectional integration achieves a BFS accuracy of 0.853 MHz under SNR of 5.22 dB and significantly reduces the measurement time by 85.86 %. Additionally, the introduced coefficient k characterizes the influence on the BGS compression, with bidirectional integration exhibiting a narrower full width at half maximum of BGS (52.74 MHz) compared to Lorentzian curve fitting (67.45 MHz).

Multi-Event Location Denoising Scheme for φ-OTDR Based on FFDNet Network

In order to improve the signal-to-noise ratio (SNR) of vibration sensing in the phase-sensitive optical time-domain reflectometer (φ-OTDR) system, a fiber sensing signal processing method based on the FFDNet convolutional neural network is proposed in this paper. In the network, the concept of residual learning is introduced, which involves constructing a residual mapping and utilizing multi-layer convolutional neural networks to learn the noise distribution present in the original image. The denoised result can be obtained by subtracting the learned noise from the original image. We have built a φ-OTDR system based on coherent detection, using three PZTs as simulated vibration sources and a series of experiments at 200 Hz, with each experiment simulating a single vibration event or multiple vibration events by setting different intensities. The experimental results demonstrate that the FFDNet based fiber optic sensing signal processing method enhances the SNR to 37.84 dB, 37.11 dB, and 37.31 dB, respectively, while preserving vibration signal details more effectively than wavelet denoising and Gaussian filtering techniques. The trained FFDNet model has great potential for improving the performance of the φ-OTDR system and has some practical application value.

Digital phase shift based simulated coherence phase demodulation technology for [formula omitted]-OTDR

In this paper, we propose a digital phase demodulation scheme for phase-sensitive optical time-domain reflectometer (Φ-OTDR) systems based on simulated coherence for digital phase shift, in which three-path signals with a phase interval of 120° can be obtained by employing digital simulated orthogonal coherence for phase shift instead of using a hardware 3×3 coupler and three detectors, while the high-frequency component can be eliminated through cross differential between simulated coherence signals. On this basis, the phase variation of Rayleigh backscattering (RBS) light can be analyzed by differential cross multiplication (DCM). The performance of the proposed scheme is evaluated through experiments by detecting a series of vibration signals with various sensing distances, vibration frequencies, and waveforms. The experimental results demonstrate that the phase variation caused by external vibration can be effectively reconstructed and a strain resolution of 0.5 nɛ and the SNR of 51.03 dB can be achieved through the proposed demodulation method, which significantly simplifies the system and effectively suppresses the deviation of phase interval and noise caused by hardware demodulation.

Optical Frequency Domain Reflectometry Based on Multilayer Perceptron

We proposed an optical frequency domain reflectometry based on a multilayer perceptron. A classification multilayer perceptron was applied to train and grasp the fingerprint features of Rayleigh scattering spectrum in the optical fiber. The training set was constructed by moving the reference spectrum and adding the supplementary spectrum. Strain measurement was employed to verify the feasibility of the method. Compared with the traditional cross-correlation algorithm, the multilayer perceptron achieves a larger measurement range, better measurement accuracy, and is less time-consuming. To our knowledge, this is the first time that machine learning has been introduced into an optical frequency domain reflectometry system. Such thoughts and results would bring new knowledge and optimization to the optical frequency domain reflectometer system.

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.

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.

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.

High-Precision Measurement of Brillouin Frequency Shift in Brillouin Optical Time-Domain Reflectometer Based on Half-Peak Fitting Algorithm of Power Spectrum

The Brillouin optical time-domain reflectometer (BOTDR) system, which is based on the Brillouin frequency shift (BFS) of the scattering spectrum, has been widely applied to monitor the temperature and strain in many fields. The measurement accuracy of the BFS directly affects the monitoring and location of the temperature change or strain events. In this study, the characteristics of the least-squares fitting optimal solution of the Brillouin power spectrum are theoretically investigated, and a half-peak fitting algorithm is proposed to measure the BFS with high accuracy and stability. In particular, it can precisely determine the double-peak spectrum and detect the position in the transition section of a temperature change or strain event. Furthermore, it can reduce calculation complexity and enhance measurement speed by dropping most of the data. For the fiber under test (FUT) with a length of 12-km, 200 groups of time-domain data were processed using a probe pulse of width 50 ns and sampling rate 1 GHz. The half-peak fitting algorithm increased the temperature measurement accuracy by ~1 fold, with ~20% calculation of full fitting. Meanwhile, it effectively eliminated the influence of minor peak in the double-peak spectrum, and optimized the spatial resolution of the temperature change position to 0.1-m, which is the maximum limitation by sampling rate.

Parametric Decay Instabilities during Electron Cyclotron Resonance Heating of Fusion Plasmas, Problems and Possibilities

We review parametric decay instabilities (PDIs) expected in connection with electron cyclotron resonance heating (ECRH) of magnetically confined fusion plasmas, with a specific focus on conditions relevant for the ITER tokamak. PDIs involving upper hybrid (UH) waves are likely to occur in O-mode ECRH scenarios at ITER if electron density profiles allowing trapping of UH waves near the ECRH frequency are present. Such PDIs may occur near the plasma center in ITER full-field scenarios heated by 170 GHz O-mode ECRH and on the high-field side of half-field ITER plasmas heated by 110 GHz or 104 GHz O-mode ECRH. Additionally, 110 GHz O-mode ECRH of half-field ITER scenarios may have low ECRH absorption, due to the electron cyclotron resonance being located on the high-field side of the main plasma. This potentially allows PDIs driven by a significant amount of ECRH radiation reaching the UH resonance in X-mode to occur, as X-mode radiation can be generated by reflection of unabsorbed O-mode radiation from the high-field side wall. The occurrence of PDIs during ECRH may damage microwave diagnostics, such as the electron cyclotron emission and low-field side reflectometer systems at ITER, as well as complicate the calculation of heating and current drive characteristics. However, if PDIs are induced in a controlled manner, they may provide novel diagnostic tools and allow the generation of a moderate fast ion population in plasmas heated only by ECRH.

Dynamic wavelength calibration based on synchrosqueezed wavelet transform.

With the developments of the tunable laser source (TLS), there are increasing demands for high-resolution dynamic wavelength calibration in recent years. Considering mutual constraints between wide measurement range and high calibration resolution, we propose a dynamic wavelength calibration method based on an auxiliary Mach-Zehnder interferometer (MZI) and the synchrosqueezed wavelet transform (SSWT). Our proposed method can achieve a calibration resolution of 5 fm and a tuning range of 10 nm. Moreover, the measurement range and spatial resolution of the optical frequency domain reflectometer (OFDR) system are improved to ∼80 m and ∼mm, respectively. Our proposed approach can substantially reduce the subtle spectrum distortion (tens of fm) in coherent optical spectrum analyzer (COSA) systems.

Noise Reduction Based on Adaptive Prediction Fitting Algorithm for a Heterodyne Φ-OTDR System

Aiming to reduce the noise better in distributed optical fiber vibration sensing system, an adaptive predictive filtering algorithm is proposed to improve the overall signal-to-noise ratio (SNR) of vibration detection. A phase-sensitive optical time domain reflectometric ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR) system based on heterodyne detection with dual AOMs is designed to reduce the requirement of DAQ high sampling rate, and an adaptive prediction noise reduction method based on normalized least mean square (NLMS) is adopted to reduce the non-stationary noise. Experimental results show that the SNR is increased from 8.14dB to 22.31dB. Compared with moving difference average method, wavelet denoising method and Prewitt edge detection method, the SNR of the adaptive prediction noise reduction algorithm is the highest, which would have potential application in disturbance detection under a strong noise environment.

Measurement of re-entry plasma density using microwave reflectometer in laboratory.

A laboratory-scale experiment was conducted to reproduce plasma with properties similar to re-entry plasma and measure the plasma density using a microwave reflectometer system. To reproduce a similar re-entry plasma, a high-temperature refractory anode vacuum arc plasma method was used among arc plasma discharge methods, and arc plasma having high temperature, high speed, and high-density plasma characteristics was discharged inside a vacuum chamber. A hot refractory anode made of tungsten was used to show high-temperature plasma characteristics, and high-density plasma characteristics were demonstrated using re-evaporation around the anode. In addition, high-speed plasma characteristics were exhibited using a brass cathode. This kind of arc plasma discharge has a high temperature and is characterized by high fluctuation. It was determined that a microwave reflectometer system with good spatial resolution and non-invasiveness would be suitable to measure plasma with these characteristics. The reflection coefficient was measured using a reflector system by comparing the voltage between the traveling wave applied to the plasma and the reflected wave reflected by the plasma, and the technique of analyzing the plasma density using the difference between these reflection coefficients was used. In this study, the plasma density according to the pressure change was typically measured as 1012-1013cm-3, which showed a similar tendency to the result of measuring the actual re-entry plasma density.