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An automatic algorithm for surface wave dispersion curve picking based on Hessian matrix attributes.

Hou Xiaoping1, Yu Jiashun2, Yuan Jianlong1

  • 1College of Geophysics, Chengdu University of Technology, Chengdu, Sichuan, 610059, China.

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|July 2, 2025
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Summary
This summary is machine-generated.

A novel automatic method accurately picks surface wave dispersion curves using Hessian matrix attributes. This efficient algorithm enhances seismic data analysis for large-scale projects like oil and gas exploration.

Keywords:
Automatic pickingDispersion curveHessian analysisSurface wave

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Area of Science:

  • Geophysics
  • Seismology
  • Earth Sciences

Background:

  • Surface wave dispersion curve picking is crucial for seismic exploration.
  • Existing methods often require manual interaction or model training, limiting efficiency.
  • Automating this process is essential for large-scale geophysical projects.

Purpose of the Study:

  • To develop and validate a new automatic method for picking surface wave dispersion curves.
  • To assess the algorithm's performance in terms of accuracy, completeness, and efficiency.
  • To demonstrate the algorithm's practical applicability in industrial prospecting.

Main Methods:

  • Developed an algorithm based on dispersion power spectra transformed from surface waves.
  • Employed ridge searching, extraction, line segment connection, selection, and order sorting.
  • Utilized Hessian matrix attributes for automatic dispersion curve picking.

Main Results:

  • Successfully picked surface wave dispersion curves up to the 8th order on synthetic data.
  • Demonstrated superior performance compared to other methods in accuracy, completeness, anti-noise capability, and computational efficiency.
  • Obtained fundamental, first, and second-order dispersion curves from industrial prospecting data, with inversion results matching vertical seismic profiles.

Conclusions:

  • The proposed automatic method is highly effective and practical for surface wave dispersion curve picking.
  • The algorithm offers significant advantages in efficiency and accuracy for geophysical exploration.
  • Successful application in an industrial project validates its real-world utility.