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Robust regression and its application in absolute gravimeters.

M Hu1, W M Zhang1, M Zhong1

  • 1State Key Laboratory of Geodesy and Earth's Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China.

The Review of Scientific Instruments
|June 3, 2017
PubMed
Summary
This summary is machine-generated.

Robust regression using maximum-likelihood estimation improves absolute gravimeter data accuracy by identifying and rejecting outliers. This method enhances gravity measurements by adjusting weighting functions for time-distance pairs.

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Area of Science:

  • Geophysics
  • Data Analysis

Background:

  • Absolute gravimeter data processing is crucial for geophysical surveys.
  • Outliers in gravity data can significantly affect measurement accuracy.

Purpose of the Study:

  • To evaluate maximum-likelihood estimation of robust regression for processing absolute gravimeter data.
  • To compare robust regression methods with ordinary least squares and existing software.

Main Methods:

  • Maximum-likelihood estimation of robust regression.
  • Comparison with ordinary least squares and g9 processing software for FG5 gravimeter data.
  • Analysis of weighting functions for time-distance pairs.

Main Results:

  • Robust regression demonstrates improved accuracy in gravity data compared to ordinary least squares.
  • Adjusting weighting functions effectively rejects outliers, enhancing data quality.
  • A practical method for identifying outliers based on their weights is presented.

Conclusions:

  • Maximum-likelihood robust regression is a suitable method for processing absolute gravimeter data.
  • Outlier rejection through adjusted weighting functions is key to improving gravity data accuracy.
  • The presented outlier identification method offers a convenient approach for data refinement.