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Data Agreement Analysis And Correction of Comparative Geomagnetic Vector ObservationsResearch Article • February 11th, 2022
A version of this preprint was published at Earth, Planets and Space on February 11th, 2022. See the published version at https://doi.org/10.1186/s40623-022-01583-9.
Data Agreement Analysis And Correction of Comparative Geomagnetic Vector ObservationsResearch Article • October 21st, 2021
Geomagnetism, similar to other areas of geophysics, is an observation-based science. Data agreement between comparative geomagnetic vector observations is one of the most important evaluation criteria for high-quality geomagnetic data. The main influencing factors affecting the agreement between comparative observational data are the attitude angle, scale factor, long-term time drift, and temperature. In this paper, we propose a method based on a genetic algorithm and linear regression to correct for these effects and use the distribution pattern of points in Bland–Altman plots with a 95% confidence interval length to qualitatively and quantitatively evaluate the agreement between the comparative observational data. In Bland–Altman plots with better agreement, that is, with the corrected data, more than 95% of the points are distributed within the 95% confidence interval and there is no obvious pattern in the distribution of the points. Meanwhile, the length of 95% confidence interval
