Towards events recognition in a distributed fiber-optic sensor system: Kolmogorov-Zurbenko filtering

TL;DR

This paper presents a de-noising approach using Kolmogorov-Zurbenko filtering for improved event recognition in signals from distributed fiber-optic vibration sensors, demonstrated through seismic background experiments.

Contribution

Introduces a novel de-noising method based on Kolmogorov-Zurbenko filtering for fiber-optic event recognition, enhancing detection of seismic events.

Findings

Effective recognition of multiple event classes in seismic signals

Kolmogorov-Zurbenko filtering improves signal clarity

Method demonstrates practical viability in real sensor data

Abstract

The paper is about de-noising procedures aimed on events recognition in signals from a distributed fiber-optic vibration sensor system based on the phase-sensitive optical time-domain reflectometry. We report experimental results on recognition of several classes of events in a seismic background. A de-noising procedure uses the framework of the time-series analysis and Kolmogorov-Zurbenko filtering. We demonstrate that this approach allows revealing signatures of several classes of events.