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<p>In dynamic gravimetry, it is important to enhance measurement accuracy while preserving the resolution of the results. Based on an analysis of the error mechanisms inherent in strapdown dynamic gravimetry, the error sources contributing to dynamic measurement errors were identified, and an artificial neural network was proposed to assess the influence of each error source on the measurement results. The effectiveness of this approach was validated using strapdown airborne gravimetry data acquired under undulated flight conditions. The results demonstrate that, with 160 s filtering, the repeatability of gravity measurements improved from 1.41 mGal to 0.55 mGal, representing a significant enhancement in repeatability under fluctuating flight conditions while maintaining the original spatial resolution.</p>

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Dynamic error elimination method of strapdown gravimeter based on artificial neural network

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • Shao-kun Cai,
  • Yang Yu,
  • Rui-hang Yu,
  • Zhi-ming Xiong,
  • Ju-liang Cao,
  • Yan Guo,
  • Bai-nan Yang,
  • Kai-xin Luo,
  • Mei-ping Wu

摘要

In dynamic gravimetry, it is important to enhance measurement accuracy while preserving the resolution of the results. Based on an analysis of the error mechanisms inherent in strapdown dynamic gravimetry, the error sources contributing to dynamic measurement errors were identified, and an artificial neural network was proposed to assess the influence of each error source on the measurement results. The effectiveness of this approach was validated using strapdown airborne gravimetry data acquired under undulated flight conditions. The results demonstrate that, with 160 s filtering, the repeatability of gravity measurements improved from 1.41 mGal to 0.55 mGal, representing a significant enhancement in repeatability under fluctuating flight conditions while maintaining the original spatial resolution.