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Seismic Intensity Prediction with a Low-Computational-Cost Transformer-Based Tracking Method.

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|October 29, 2025
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Summary
This summary is machine-generated.

A new low-computational-cost transformer-based visual tracking (LCCTV) method accurately predicts seismic intensity using surveillance videos. This cost-effective approach offers a scalable solution for real-time seismic intensity analysis and disaster relief guidance.

Keywords:
low costseismic intensityseismic station simulationtransformer-based visual tracking

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Area of Science:

  • Geophysics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate seismic intensity prediction is crucial for disaster relief.
  • Traditional seismograph methods face limitations in response time and cost.
  • Novel approaches are needed for efficient seismic monitoring.

Purpose of the Study:

  • To introduce a low-computational-cost transformer-based visual tracking (LCCTV) method for seismic intensity prediction.
  • To develop a novel earthquake video dataset for training and evaluating the LCCTV method.
  • To provide a cost-effective and scalable solution for seismic intensity analysis.

Main Methods:

  • A low-computational-cost transformer-based visual tracking (LCCTV) method was developed to track calibration board movement in videos.
  • A Butterworth filter was employed to smooth movement trajectories and remove low-frequency interference.
  • Seismic intensity was predicted using velocity and acceleration derived from the smoothed trajectories.

Main Results:

  • The proposed LCCTV method demonstrated superior performance compared to state-of-the-art approaches.
  • Experimental results validated the effectiveness of the LCCTV for real-time seismic intensity estimation.
  • The method successfully predicted seismic intensity based on visual tracking data.

Conclusions:

  • The LCCTV method offers a low-cost, scalable alternative for seismic intensity analysis.
  • This visual tracking approach can enhance disaster preparedness and response.
  • The developed earthquake video dataset facilitates further research in seismic monitoring.