Inverse Poisson integral semiparametric approach of estimating airborne gravity systematic error and downward continuation
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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.

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History
  • Received:April 26,2017
  • Revised:
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  • Online: July 11,2018
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