# Abnormal Vibration Identification of Metro Tunnels on the Basis of the Spatial Correlation of Dynamic Strain from Dense Measurement Points of Distributed Sensing Optical Fibers

## TL;DR

This paper introduces a method to identify abnormal vibrations in metro tunnels using spatial correlation of dynamic strain data from optical fibers, improving safety and accuracy.

## Contribution

A novel method for abnormal vibration identification using spatial correlation and dynamic strain data from dense optical fiber measurements.

## Key findings

- The proposed method effectively identifies abnormal vibrations in metro tunnels through spatial correlation analysis.
- Numerical simulations and real-world experiments confirmed the method's accuracy and reliability.
- Dynamic strain features were downscaled and updated using quadratic weighting and kernel density estimation.

## Abstract

The failure to accurately identify abnormal vibrations in protected metro areas is a serious threat to the operational safety of metro tunnels and trains, and there is currently no suitable method for effectively improving the accuracy of abnormal vibration identification. To address this issue, an accurate method for identifying abnormal vibrations in a metro reserve based on spatially correlated dense measurement points is proposed. First, by arranging distributed optical fibers along the longitudinal length of a tunnel, dynamic strain vibration signals are extracted via phase-sensitive optical time-domain reflectometry analysis, and analysis of variance (ANOVA) and Pearson correlation analysis are used to jointly downscale the dynamic strain features. On this basis, a spatial correlation between the calculated values of the features of the target measurement points to be updated and its adjacent measurement points is constructed, and the spatial correlation credibility of the dynamic strain features between the dense measurement points and the target measurement points to be updated is calculated via quadratic function weighting and kernel density estimation methods. The weights are calculated, and the eigenvalues of the target measurement points are updated on the basis of the correlation credibility weights between the adjacent measurement points. Finally, a support vector machine (SVM) and back propagation (BP) identification model for the eigenvalues of the target measurement points are constructed to identify the dynamic strain eigenvalues of the abnormal vibrations in the underground tunnel. Numerical simulations and an experiment in an actual tunnel verify the effectiveness of the proposed method.