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Analyzing Seismic Signal Using Support Vector Machine for Vehicle Motion Detection

  • Thang Duong NhatEmail author
  • Mai Nguyen Thi Phuong
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 257)

Abstract

A system to process seismic signals of vehicles passing between two sensor stations had been developed and experimented. To evaluate the feasibility of the system before field test with a real vehicle and to support the classification model with artificial data later, the input seismic data were simulated from Green’s method function that accounts only for Rayleigh surface wave. The system using the Machine Learning Classification method SVM to classify data collected from two stations at any time have the state of passed or not. By processing the signal, the system could detect whether the vehicle had passed the crossing line or not with the accuracy of 99.10% for simulated data and 94.22% for experiment data. The experiment and results suggested that processing seismic signals to monitor control lines is feasible.

Keywords

Machine Learning Seismic signal Motion detection 

Notes

Acknowledgment

We would like to express our gratitude to the staff members in the Department of Precision Mechanical and Optical Engineering, Hanoi University of Science and Technology, Vietnam for supporting the research. We also would like to show our gratitude toward Mr. Anh Nguyen, Mr. Binh Nguyen and Mr. Phuong Le for their help in building the hardware for the experiment.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Center for Training of Excellent StudentsHanoi University of Science and TechnologyHanoiVietnam
  2. 2.Department of Precision Mechanical and Optical EngineeringHanoi University of Science and TechnologyHanoiVietnam

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