An online two-stage adaptive algorithm for strain profile estimation from noisy and abruptly changing BOTDR data and application to underground mines

G. Soto, J. Fontbona, R. Cortez, L. Mujica

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Strain measurement using BOTDR (Brillouin Optical Time-Domain Reflectometry) is nowadays a standard tool for structural health monitoring. In this context, weak data quality and noise, usually owed to defective fiber installation, hinders discriminating actual level shifts from outliers and might entail a biased risk assessment. We propose a novel online adaptive algorithm for strain profile estimation in strain time series with abrupt and gradual changes and missing data. It relies on a convolution filter in Brillouin spectrum domain and a smoothing technique in time domain. In simulated data, the convolution filter is shown to reduce strain measurement uncertainty by up to 8 times the strain resolution. The two-stage method is illustrated with systematic outliers removal from real data of a Chilean copper mine and the improvement of the associated gain spectrum quality by up to 18 dB in SNR terms.

Original languageEnglish
Pages (from-to)340-351
Number of pages12
JournalMeasurement: Journal of the International Measurement Confederation
Volume92
DOIs
Publication statusPublished - 1 Oct 2016

Keywords

  • Abrupt changes
  • BOTDR
  • Brillouin gain spectrum
  • Outliers
  • Smoothing
  • Strain
  • Time series

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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