| 引用本文: | 赵启龙,李建成,徐新禹,等.评估航空重力系统误差及向下延拓的逆泊松半参数方法.[J].国防科技大学学报,2018,40(3):49-54.[点击复制] |
| ZHAO Qilong,LI Jiancheng,XU Xinyu,et al.Inverse Poisson integral semi parametric approach of estimating airborne gravity systematic error and downward continuation[J].Journal of National University of Defense Technology,2018,40(3):49-54[点击复制] |
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| 评估航空重力系统误差及向下延拓的逆泊松半参数方法 |
| 赵启龙1,2, 李建成1, 徐新禹1, 于男1 |
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(1. 武汉大学 测绘学院， 湖北 武汉 430079;2.
2. 武汉大学 地球空间环境与大地测量教育部重点实验室， 湖北 武汉 430079)
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| 摘要: |
| 常用航空重力系统误差事后处理方法需外部重力数据，但很多地区无外部重力数据。研究发现，半参数模型可在无外部数据时估计系统误差。先用自然样条函数为系统误差建模，后用补偿最小二乘法和光滑参数求解，最后用广义交叉核实法（不需要先验信息）选取光滑参数。将半参数模型用于向下延拓逆泊松积分，建立逆泊松半参数混合模型，既可无外部重力时估计系统误差，又可向下延拓。实验结果表明：无外部重力时逆泊松积分和最小二乘配置法受系统误差影响最大，向下延拓精度最差；正则化算法可减弱系统误差影响，向下延拓精度较好；逆泊松半参数混合模型可估计系统误差，向下延拓精度最好。 |
| 关键词: 半参数模型 逆泊松积分 向下延拓 系统误差 |
| DOI：10.11887/j.cn.201803008 |
| 投稿日期：2017-04-26 |
| 基金项目:国家重点基础研究发展计划资助项目(2013CB733301)；国家高技术研究发展计划资助项目(2013AA122502)；国家自然科学基金资助项目(41374022)；中央高校基本科研业务费专项资金资助项目(2015214020202) |
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| Inverse Poisson integral semi parametric approach of estimating airborne gravity systematic error and downward continuation |
| ZHAO Qilong1,2, LI Jiancheng1, XU Xinyu1, YU Nan1 |
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(1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;2.
2. Key Laboratory of Geospace Environment and Geodesy of Ministry of Education, Wuhan University, Wuhan 430079, China)
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| Abstract: |
| The existing systematic errors processing method of airborne gravity demands external gravity data, but many areas do not have external gravity data. However, semi parameter model can estimate the systematic errors without external data. Firstly, the systematic errors were modeled by using natural spline function. Then the compensation least squares method was used to estimate the parameter and the natural spline function. The smooth parameter was used to balance them. More importantly, the generalized cross validation method to determine the smooth parameter does not need prior information. Therefore, the semi parameter model was applied in the inverse Poisson integral to estimate systematic errors and downward continuation in one step. The numerical test results show that the inverse Poisson integral and least square collocation cannot estimate the systematic errors. The regularization method based on the inverse Poisson integral can reduce systematic error effect. The semi parameter combine inverse Poisson integral model can estimate the systematic errors and improve downward continuation accuracy at the same time without external gravity data. |
| Keywords: semi-parametric model inverse Poisson integral downward continuation systematic errors |
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