Improved Generalized Cross-Validation and Unbiased Predictive Risk Estimator Methods Using the RGSVD: Application to Inversion of Potential Field Data

• Published in: Applied sciences 2021-07, Vol.11 (14), p.6326
• Main Authors: Fang, Yuan, Wang, Jun, Meng, Xiaohong, Tang, Hanhan
• Format: Article
• Language: English
• Subjects: Algorithms; Approximation; Computer applications; Computing time; Decomposition; generalized cross-validation; Inverse problems; Inversion; Methods; Potential fields; randomized generalized singular value decomposition; Regularization; regularization parameter; unbiased predictive risk estimator
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11146326

The inversion of potential field data has widely utilized the generalized cross-validation (GCV) and the unbiased predictive risk estimator (UPRE) methods to determine the regularization parameter. However, these two methods are time-consuming and it is difficult for them to determine the optimal linear search range including the optimal regularization. To solve these problems, this article improves the GCV and UPRE methods using the RGSVD (randomized generalized singular value decomposition) algorithm. The improved methods first use the randomized algorithm to compute an approximate generalized singular value decomposition (GSVD) with less computational time. Then, the optimal linear search range is determined based on the generalized singular values. Finally, the GCV and the UPRE functions are efficiently computed on the basis of the results from the RGSVD algorithm. In this way, the GCV and UPRE methods using the RGSVD algorithm are able to determine the optimal regularization parameter fast and effectively. One comparative test shows the effectiveness and efficiency of the GCV and the UPRE methods using the RGSVD algorithm.