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Vibration error correction in absolute gravity measurement using BP neural network.

Yongzhuo Niu1, Qiong Wu2, Yang Zhang1

  • 1Institute of Geophysics, China Earthquake Administration, Beijing, 100081, China.

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|March 19, 2026
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Summary

This study introduces a novel method to reduce errors in high-precision gravity measurements caused by ground vibrations. By using an Adam-optimized neural network, the technique accurately corrects time errors, improving measurement precision.

Keywords:
Absolute gravity measurementBP neural networkHigh-precision measurementVibration error

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Area of Science:

  • Geophysics
  • Signal Processing
  • Machine Learning

Background:

  • High-precision absolute gravity measurements are susceptible to errors from ground vibrations.
  • Vibration interference significantly impacts the accuracy of falling body trajectory reconstruction.
  • Existing methods struggle to effectively isolate and correct for vibration-induced errors.

Purpose of the Study:

  • To develop an advanced vibration error correction method for absolute gravity measurements.
  • To enhance the accuracy and reliability of gravity data acquisition in noisy environments.
  • To investigate the application of neural networks optimized with the Adam algorithm for geophysical data processing.

Main Methods:

  • Constructed a vibration error model to analyze the influence of vibration modes on trajectory reconstruction.
  • Developed a Backpropagation (BP) neural network model to predict time coordinate errors based on vibration signals.
  • Optimized the BP neural network using the Adam algorithm for improved convergence and prediction accuracy.
  • Validated the method through simulations and field absolute gravity observation experiments.

Main Results:

  • Verified a strong correlation between time errors and vibration signals in gravity measurements.
  • The proposed method effectively separates vibration interference components from the primary signals.
  • Achieved high measurement accuracies: 1.51 µGal at H03, 1.30 µGal at H09, and 3.01 µGal at H20.
  • Demonstrated superior performance compared to traditional methods in vibration error mitigation.

Conclusions:

  • The Adam algorithm-optimized BP neural network provides an effective solution for correcting vibration errors in high-precision absolute gravity measurements.
  • This approach significantly enhances measurement accuracy and reliability, particularly in environments with substantial ground vibrations.
  • The method holds promise for advancing geophysical surveying and monitoring applications.