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Evaluation and Comparison of the Processing Methods of Airborne Gravimetry Concerning the Errors Effects on Downward Continuation Results: Case Studies in Louisiana (USA) and the Tibetan Plateau (China)

机译:误差对向下延展结果影响的航空重量分析方法的评估和比较:以路易斯安那州(美国)和青藏高原(中国)为例

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Gravity data gaps in mountainous areas are nowadays often filled in with the data from airborne gravity surveys. Because of the errors caused by the airborne gravimeter sensors, and because of rough flight conditions, such errors cannot be completely eliminated. The precision of the gravity disturbances generated by the airborne gravimetry is around 3–5 mgal. A major obstacle in using airborne gravimetry are the errors caused by the downward continuation. In order to improve the results the external high-accuracy gravity information e.g., from the surface data can be used for high frequency correction, while satellite information can be applying for low frequency correction. Surface data may be used to reduce the systematic errors, while regularization methods can reduce the random errors in downward continuation. Airborne gravity surveys are sometimes conducted in mountainous areas and the most extreme area of the world for this type of survey is the Tibetan Plateau. Since there are no high-accuracy surface gravity data available for this area, the above error minimization method involving the external gravity data cannot be used. We propose a semi-parametric downward continuation method in combination with regularization to suppress the systematic error effect and the random error effect in the Tibetan Plateau; i.e., without the use of the external high-accuracy gravity data. We use a Louisiana airborne gravity dataset from the USA National Oceanic and Atmospheric Administration (NOAA) to demonstrate that the new method works effectively. Furthermore, and for the Tibetan Plateau we show that the numerical experiment is also successfully conducted using the synthetic Earth Gravitational Model 2008 (EGM08)-derived gravity data contaminated with the synthetic errors. The estimated systematic errors generated by the method are close to the simulated values. In addition, we study the relationship between the downward continuation altitudes and the error effect. The analysis results show that the proposed semi-parametric method combined with regularization is efficient to address such modelling problems.
机译:如今,山区的重力数据空白经常被空中重力调查的数据所填补。由于机载重力传感器引起的误差,以及由于飞行条件恶劣,这种误差无法完全消除。空中重量分析法产生的重力扰动的精度约为3-5 mgal。使用机载重量分析仪的主要障碍是向下连续引起的误差。为了改善结果,例如来自表面数据的外部高精度重力信息可以用于高频校正,而卫星信息可以用于低频校正。表面数据可以用来减少系统误差,而正则化方法可以减少向下连续的随机误差。机载重力调查有时在山区进行,而这种调查的世界上最极端的区域是青藏高原。由于没有可用于此区域的高精度表面重力数据,因此无法使用上述涉及外部重力数据的最小化误差的方法。为了抑制青藏高原的系统误差效应和随机误差效应,我们提出了一种半参数向下连续法与正则化相结合的方法。即,不使用外部高精度重力数据。我们使用美国国家海洋与大气管理局(NOAA)的路易斯安那州航空重力数据集来证明新方法有效地起作用。此外,对于青藏高原,我们证明了使用合成地球重力模型2008(EGM08)导出的,被合成误差污染的重力数据也成功进行了数值实验。该方法产生的估计系统误差接近模拟值。此外,我们研究了向下持续高度与误差效应之间的关系。分析结果表明,所提出的半参数化方法与正则化相结合可以有效地解决此类建模问题。

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