An online two-stage adaptive algorithm for strain profile estimation from noisy and abruptly changing BOTDR data and application to underground mines. (October 2016) - Soto, G.; Fontbona, J.; Cortez, R.; Mujica, L.
Highlights: An two-stage adaptive algorithm for strain profile estimation is proposed. The algorithm estimates strain time series with abrupt and gradual changes. The algorithm is robust against missing data and removes outliers. 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.