Abstract
Distributed temperature sensing (DTS) systems can be used to estimate the temperature along optic fibers of several kilometers at a submeter interval. DTS systems function by shooting laser pulses through a fiber and measuring its backscatter intensity at two distinct wavelengths in the Raman spectrum. The scatteringloss coefficients for these wavelengths are temperaturedependent, so that the temperature along the fiber can be estimated using calibration to fiber sections with a known temperature. A new calibration approach is developed that allows for an estimate of the uncertainty of the estimated temperature, which varies along the fiber and with time. The uncertainty is a result of the noise from the detectors and the uncertainty in the calibrated parameters that relate the backscatter intensity to temperature. Estimation of the confidence interval of the temperature requires an estimate of the distribution of the noise from the detectors and an estimate of the multivariate distribution of the parameters. Both distributions are propagated with Monte Carlo sampling to approximate the probability density function of the estimated temperature, which is different at each point along the fiber and varies over time. Various summarizing statistics are computed from the approximate probability density function, such as the confidence intervals and the standard uncertainty (the estimated standard deviation) of the estimated temperature. An example is presented to demonstrate the approach and to assess the reasonableness of the estimated confidence intervals. The approach is implemented in the opensource Python package “dtscalibration”.
Original language  English 

Article number  2235 
Number of pages  21 
Journal  Sensors (Switzerland) 
Volume  20 
Issue number  8 
DOIs  
Publication status  Published  2020 
Bibliographical note
Corrigendum: DOI 10.3390/s21030912. The original article has been updated.Keywords
 Calibration
 Confidence intervals
 Distributed temperature sensing
 DTS
 Fiber optic
 Raman
 Stokes
 Temperature
 Uncertainty
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DTS measurements supporting a DTS calibration paper
des Tombe, B. F. (Creator), TU Delft  4TU.ResearchData, 18 Mar 2019
DOI: 10.4121/UUID:71B5C3C241054F4FBD1ED7C56732A665
Dataset/Software: Dataset