## 5 Conclusions  
Most geophysical phenomena are associated with mass transport, and gravity signals can be observed in ground-based gravity measurements. Among the various errors associated with spring-based gravimeters, the nonlinear drift rate and inaccurate SF are two of the most prominent. In practical, these can be estimated using redundant gravity observations, including increasing the number of AG values, increasing the frequency of the baseline calibration, and adopting optimized adjustment methods. In this study, we adopted the MBGA method to quantified the errors caused by nonlinear drift and SF bias. Through processing gravity data obtained by the southeastern Tibetan Plateau survey network during 2018-2020, the following conclusions were derived. In comparison with the CLS method, which considers a linear drift rate, the drift rate of CG-5 and LCR-G relative gravimeters, used in the study, showed nonlinear drift characteristics when using either the MBGA method or the BGA method. Additionally, the amplitude of the drift rate showed obvious differences among the different instruments, i.e., the CG-5 gravimeter amplitude was much larger than that of the LCR-G gravimeters. Moreover, the nonlinear drift rate variations of the two CG-5 gravimeters were not synchronous. With the increase of service life, the drift rate of the CG-5 instruments decreased gradually and stabilized at 30-40 \(\upmu\)Gal/h. In contrast, the drift rate of the LCR-G was relatively small, i.e., generally no greater than 1.5 \(\upmu\)Gal/h. By contrasting the GD residuals obtained using the CLS and MBGA methods, it was found that the GD residuals of the CLS method have obvious nonrandom signals, and that this change is synchronized with the variation of the drift rate. Conversely, the GD residuals obtained using the BGA and MBGA methods present as random white noise signals that have no significant correlation with the variation of drift rate. Thus, the gravity adjustment based on the Bayesian estimation principle can remove errors caused by the nonlinear drift of the instrument. No fewer than two AG stations are needed for SF calibration, further, the gravity difference between the two AG stations should cover no less than half that of the maximum GD in the network. And when the number of AG stations involved in calibration remains the same, the ABIC value can be considered as the criterion to judge the calibration results. Analysis of the correlation between the GD and mutual difference of the GD from the two gravimeter pairs revealed an obvious correlation from inaccurate SF, while the correlation obtained using the MBGA method was significantly weakened. Therefore, the optimized SFs from the MBGA method are more effective in reducing the system errors caused by inaccurate SFs. The adjustment results indicated that the average difference of the SFs of the LCR-G gravimeters was \(<\)63 ppm, while the average difference was \(>\)150 ppm for the CG-5 instruments owing to the different characteristics of the spring. Moreover, the variation of SF is also associated with instrumental drift. Cross validation of the AG values revealed that the optimized SFs calculated using the MBGA method are better than those derived using the BGA method. Comparison of the difference of GVs among the different adjustment methods indicated that the uncertainty caused by the nonlinear drift rate (\(\pm\)12 \(\upmu\)Gal) is smaller than that associated with accurate SFs (\(-\)40 to \(+\)60 \(\upmu\)Gal). In summary, under the constraint of AG values, the MBGA method can reduce the GV uncertainty caused by nonlinear instrumental drift and inaccurate SFs without calibration using the long-baseline, thereby allowing high-precision terrestrial microgravity measurements to be obtained, and lay a solid foundation for the application of time-varying gravity data in seismic monitoring and geoscience research. Conceptualization, Q.Z., S.C. and X.Y. ; Methodology, S.C. and J.Y. ; Software, S.C. and J.Y. ; Validation, Q.Z., J.Y., S.C., Z.C. and D.L.