Data agreement analysis and correction of comparative geomagnetic vector observations

Abstract Geomagnetism, similar to other areas of geophysics, is an observation-based science. Data agreement between comparative geomagnetic vector observations one the most important evaluation criteria for high-quality data. The main influencing factors affecting observational data are attitude angle, scale factor, long-term time drift, and temperature. In this paper, we propose a method based on genetic algorithm linear regression correct these effects use distribution pattern points in Bland–Altman plots with 95% confidence interval length qualitatively quantitatively evaluate better agreement, that is, corrected data, more than distributed within there no obvious points. Meanwhile, decreased significantly after correction. presented here has positive instrumentation detection would enhance robustness observatory by bringing quality backup variometer line primary variometer. Graphical