Machine learning assisted BOFDA for simultaneous temperature and strain sensing in a standard optical fiber.

We report, to our knowledge for the first time on simultaneous distributed temperature and strain sensing in a standard telecom optical fiber using a machine learning assisted Brillouin frequency domain analysis (BOFDA) system. The well-known temperature and strain cross-sensitivity problem is addressed by developing a BOFDA system with a high signal-to-noise ratio and applying machine learning. The spectrum consists of four highly resolved peaks, whose Brillouin frequency shifts are extracted and serve as features for the machine learning algorithms. The spectra result from a 450-m standard SMF-28 optical fiber, and particularly from a segment of 30 m. This fiber segment is coiled around a stretcher and placed in a climate chamber. The applied temperature and strain values range from 20 °C to 40 °C and from 0 µÉ› to 1380 µÉ›, respectively. The total measurement time to achieve a high SNR and resolve four peaks with a spatial resolution of 6 m is 16 min. To discriminate temperature and strain effects, simple frequentist and more sophisticated Bayesian-based algorithms are employed with the powerful Gaussian process regression (GPR) delivering the best performance in terms of temperature and strain errors, which are found to be 2 °C and 45 µÉ›, respectively. These errors are calculated using leave-one-out cross-validation, so that an unbiased estimation of the sensor's performance is provided.

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.

Blast-Assisted Subsurface Characterisation Using a Novel Distributed Acoustic Sensing Setup Based on Geometric Phases.

A novel DAS setup based on geometric phases in coherent heterodyne detection is applied for the first time to the characterisation of the Earth's subsurface. In addition, an optimisation of the proposed setup in terms of its spatial resolution is also presented for the first time. The surface waves are generated by strong blasts of 25 kg of explosives at a dedicated test site. A 10 km dark fiber link in the vicinity of the test site connected to the test setup records the resulting strain signals. The spike-free and low-noise strain data thus obtained minimize post-processing requirements, making the setup a candidate for real-time seismic monitoring. An analysis of the dispersion characteristics of the generated surface waves is performed using a recently reported optimised seismic interferometric technique. Based on the dispersion characteristics, the shear wave velocities of the surface waves as a function of the depth profile of the Earth's crust are determined using an optimised evolutionary algorithm.

Geometric phase in distributed fiber optic sensing.

The geometric phase in the beat signal from coherent interference of two frequency-offset light beams is measured using a novel distributed optical fiber sensing setup. In a fiber optic medium, with changing beam intensities, to the best of our knowledge, ours is the first measurement of the mentioned geometric phase. Experimental results of applying a 100-Hz sinusoidal stimulus to a polarization scrambler and a piezoelectric transducer inline to an optical fiber are presented. The results may enable novel distributed fiber sensing techniques.

Distributed humidity fiber-optic sensor based on BOFDA using a simple machine learning approach.

We report, to our knowledge for the first time, on distributed relative humidity sensing in silica polyimide-coated optical fibers using Brillouin optical frequency domain analysis (BOFDA). Linear regression, which is a simple and well-interpretable algorithm in machine learning and statistics, is utilized. The algorithm is trained using as features the Brillouin frequency shifts and linewidths of the fiber's multipeak Brillouin spectrum. To assess and improve the effectiveness of the regression algorithm, we make use of machine learning concepts to estimate the model's uncertainties and select the features that contribute most to the model's performance. In addition to relative humidity, the model is also able to simultaneously provide distributed temperature information addressing the well-known cross-sensitivity effects.

Distributed Humidity Sensing in Concrete Based on Polymer Optical Fiber.

We present a preliminary investigation on distributed humidity monitoring during the drying process of concrete based on an embedded polymer optical fiber (POF). The water dissipated into the POF changes several properties of the fiber such as refractive index, scattering coefficient and attenuation factor, which eventually alters the Rayleigh backscattered light. The optical time domain reflectometer (OTDR) technique is performed to acquire the backscattered signal at the wavelengths 650 nm and 500 nm, respectively. Experimental results show that the received signal increases at 650 nm while the fiber attenuation factor clearly increases at 500 nm, as the concrete dries out. In the hygroscopic range, the information retrieved from the signal change at 650 nm agrees well with the measurement result of the electrical humidity sensors also embedded in the concrete sample.

Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis.

To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential.

Distributed Fiberoptic Sensor for Simultaneous Humidity and Temperature Monitoring Based on Polyimide-Coated Optical Fibers.

Along temperature, humidity is one of the principal environmental factors that plays an important role in various application areas. Presented work investigates possibility of distributed fiberoptic humidity monitoring based on humidity-induced strain measurement in polyimide (PI)-coated optical fibers. Characterization of relative humidity (RH) and temperature response of four different commercial PI- and one acrylate-coated fiber was performed using optical backscattering reflectometry (OBR). The study addresses issues of temperature-humidity cross-sensitivity, fiber response stability, repeatability, and the influence of annealing. Acrylate-coated fiber exhibited rather unfavorable nonlinear RH response with strong temperature dependence, which makes it unsuitable for humidity sensing applications. On the other hand, humidity response of PI-coated fibers showed good linearity with fiber sensitivity slightly decreasing at rising temperatures. In the tested range, temperature sensitivity of the fibers remained humidity independent. Thermal annealing was shown to considerably improve and stabilize fiber RH response. Based on performed analysis, a 20 m sensor using the optimal PI-coated fibers was proposed and constructed. The sensor uses dual sensing fiber configuration for mutual decoupling and simultaneous measurement of temperature and RH variations. Using OBR, distributed dual temperature-RH monitoring with cm spatial resolution was demonstrated for the first time.

Enhanced Distributed Fiber Optic Vibration Sensing and Simultaneous Temperature Gradient Sensing Using Traditional C-OTDR and Structured Fiber with Scattering Dots.

We present results demonstrating several beneficial effects on distributed fiber optic vibration sensing (DVS) functionality and performance resulting from utilizing standard single mode optical fiber (SMF) with femtosecond laser-inscribed equally-spaced simple scattering dots. This modification is particularly useful when using traditional single-wavelength amplitude-based coherent optical time domain reflectometry (C-OTDR) as sensing method. Local sensitivity is increased in quasi-distributed interferometric sensing zones which are formed by the fiber segments between subsequent pairs of the scattering dots. The otherwise nonlinear transfer function is overwritten with that of an ordinary two-beam interferometer. This linearizes the phase response to monotonous temperature variations. Furthermore, sensitivity fading is mitigated and the demodulation of low-frequency signals is enabled. The modification also allows for the quantitative determination of local temperature gradients directly from the C-OTDR intensity traces. The dots' reflectivities and thus the induced attenuation can be tuned via the inscription process parameters. Our approach is a simple, robust and cost-effective way to gain these sensing improvements without the need for more sophisticated interrogator technology or more complex fiber structuring, e.g., based on ultra-weak FBG arrays. Our claims are substantiated by experimental evidence.