Least Squares Collocation-Tikhonov Regularization Method for the Downward Continuation of Airborne Gravity Data

The covariance matrix that least-squares collocation is used to the downward continuation of airborne gravity data is ill-conditioned, which influences the reliability and precision of computation results. To solve this problem, the least squares collocation-Tikhonov regularization method for the downward continuation of airborne gravity data is proposed. The functional relationship between airborne gravity data and ground gravity data is established by using the global covariance function model, and the Tikhonov regularization method that the Generalized Cross-Validation (GCV) method is used to select the regularization parameter is introduced, which can improve the ill conditioned covariance matrix and inhibit the amplification impact of ill matrix on observational error. Based on the EGM2008 gravity model, the simulation experiments that airborne gravity anomalies are downward continued to calculate corresponding ground gravity anomalies in mountainous, hill, and sea areas are designed, and the results validate the effectiveness of the proposed method.