ABSTRACT
Distributed acoustic sensing (DAS) is a powerful tool thanks to its ease of use, high spatial and temporal resolution, and sensitivity. Growing demand for long-distance distributed seismic sensing (DSeiS) measurements, in conjunction with the development of efficient algorithms for data processing, has led to an increased interest in the technology from industry and academia. Machine-learning-based data processing, however, necessitates tedious in situ calibration experiments that require significant effort and resources. In this Letter, a geophysics-driven approach for generating synthetic DSeiS data is described, analyzed, and tested. The generated synthetic data are used to train DSeiS classification algorithms. The approach is validated by training an artificial neural-network-based classifier using synthetic data and testing its performance on experimental DSeiS records. Accuracy is greatly improved thanks to the incorporation of a geophysical model when generating training data.
ABSTRACT
Distributed acoustic sensing (DAS) via fiber-optic reflectometry techniques is finding more and more applications in recent years. In many of these applications, the position of detected acoustic or seismic sources is defined with a single longitudinal coordinate which specifies the distance between the detection point in the fiber to the DAS interrogator. In this paper we describe a DAS system which is intended to operate in a fluid (air or water) and to detect and localize moving objects, with three spatial coordinates, using the acoustic waves they generate or reflect and their Doppler shifts. The new method uses optical frequency domain reflectometry (OFDR) and lumped Rayleigh reflectors (LRR's) to ensure sufficiently high sensitivity for operation in fluid media. The new method was used to track a narrowband (CW) signal source.
ABSTRACT
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(N2) 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.
ABSTRACT
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.
