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November 30, 2025Seismological Research LettersOpen Access

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

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Authors

CCChengping ChaiOMOmar MarcilloMMMonica Maceira

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Overview

Analysis detects ground vibrations from vehicles using machine-learning algorithms, indicating improved monitoring solutions in challenging conditions.

Key Points

  • Seven distinct cluster labels were identified, linking vehicle activity to ground vibrations and rainfall events.
  • The approach demonstrated improved accuracy over k-means clustering in detecting vehicle signals from seismic data.
  • Continuous seismic data collected at Oak Ridge National Laboratory highlights the utility of machine-learning algorithms in vehicle monitoring.
  • Findings support the idea that seismic data analysis can enhance monitoring systems, especially during adverse weather conditions.

Cite This Study

Chai et al. (2025) studied this question.

synapsesocial.com/papers/692b94581d383f2b2a378ffbhttps://doi.org/10.1785/0220250202
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  1. 1Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques2025
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