地震研究

针对在陆地百千米以上的时变流动重力观测网络中,相对重力仪非线性漂移率和仪器性能偏差是影响观测结果精度的主要误差来源这一问题,以1990—2022年云南流动重力观测数据为例,对比传统最小二乘平差方法和贝叶斯平差方法得到的非线性漂移率结果,系统测评了贝叶斯估计方法对于提高重力平差结果精度的有效性。结果表明,可以通过贝叶斯优化方法给出合适的仪器权重和非线性漂移率,减少多台相对重力仪性能差异造成的误差和传统线性漂移计算方法造成的有效漂移遗失,提高平差结果的精度。

In the time-varying gravity observation network covering hundreds kilometers of land,the nonlinear drift rate of the relative gravimeter and the instrument performance deviation are the main error sources that affect the accuracy of the observational results.In this paper,the drift rates of the mobile-gravity observational data from 1990 to 2022 in Yunnan are calculated respectively with the Bayesian gravity adjustment method and the traditional least squares adjustment method.Then the non-linear dirft rate results from these two methods are compared,and the effectiveness of the Bayesian estimation method in improving the accuracy of gravity adjustment results is systematically evaluated.The results show that the appropriate instrument weight and nonlinear drift rate can be given by the Bayesian optimization method,in order to reduce the error caused by the performance difference of multiple relative gravimeters,and reduce the effective-drift loss caused by the traditional linear drift calculation method.In this way the accuracy of the adjustment results is improved.

引言
1 数学模型
2 数据分析
3 讨论
4 结论

表1 ADJ计算的漂移率与BAY计算的漂移率均值对比<br/>Tab.1 Comparison between drift rate calculated by the ADJ method and average drift rate calculated by the BAY method

表1 ADJ计算的漂移率与BAY计算的漂移率均值对比
Tab.1 Comparison between drift rate calculated by the ADJ method and average drift rate calculated by the BAY method

图1 2020-03期观测网示意图<br/>Fig.1 Schematic diagram of observation network during the 2023-03 surveying period

图1 2020-03期观测网示意图
Fig.1 Schematic diagram of observation network during the 2023-03 surveying period

图2 2020-03测期所有测段(a)和测线(b)漂移率<br/>Fig.2 Drift rate of all survey sections(a)and survey lines(b)during the 2020-03 surveying period

图2 2020-03测期所有测段(a)和测线(b)漂移率
Fig.2 Drift rate of all survey sections(a)and survey lines(b)during the 2020-03 surveying period

图3 2020-03测期使用ADJ及BAY计算得到的漂移率<br/>Fig.3 Drift rates calculated by the ADJ method and the BAY method during the 2020-03 surveying period

图3 2020-03测期使用ADJ及BAY计算得到的漂移率
Fig.3 Drift rates calculated by the ADJ method and the BAY method during the 2020-03 surveying period

表2 ADJ和BAY计算的点值精度对比统计<br/>Tab.2 Comparison of the accuracy of the point values calculated by the ADJ method and the BAY method

表2 ADJ和BAY计算的点值精度对比统计
Tab.2 Comparison of the accuracy of the point values calculated by the ADJ method and the BAY method

图4 2020-03期ADJ(a)及BAY(b)计算结果点值误差空间分布<br/>Fig.4 Spatial distribution of the point-value error calculated by the ADJ method(a) and the BAY method(b)in the 2020-03 surveying period

图4 2020-03期ADJ(a)及BAY(b)计算结果点值误差空间分布
Fig.4 Spatial distribution of the point-value error calculated by the ADJ method(a) and the BAY method(b)in the 2020-03 surveying period

图5 ADJ(a)及BAY(b)计算的2020年3月至2021年3月相对重力变化趋势图<br/>Fig.5 The trend chart of the relative gravity variation from March 2020 to March 2021 calculated by the ADJ method(a)and the BAY method(b)

图5 ADJ(a)及BAY(b)计算的2020年3月至2021年3月相对重力变化趋势图
Fig.5 The trend chart of the relative gravity variation from March 2020 to March 2021 calculated by the ADJ method(a)and the BAY method(b)

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备注

引言

1 数学模型

2 数据分析

3 讨论

4 结论

期刊信息