Sparse Spherical Harmonic Component Selection for Gravity Field Modeling.
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| Title: | Sparse Spherical Harmonic Component Selection for Gravity Field Modeling. |
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| Authors: | Qian, Nijia1,2 (AUTHOR), Chang, Guobin1,2,3 (AUTHOR) guobinchang@cumt.edu.cn, Zhang, Xun1,3 (AUTHOR), Feng, Yong1,2,4 (AUTHOR), Yang, Dehu1,2,4 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1488. 26p. |
| Subjects: | Spherical harmonics, Gravitational fields, Regularization parameter, Feature selection, Estimation theory, Tikhonov regularization, Statistical models |
| Abstract: | Highlights: What are the main findings? Sparse regularization using Lasso and adaptive Lasso can perform spherical harmonic component selection in gravity field modeling while preserving modeling accuracy comparable to conventional Tikhonov regularization. In the closed-loop simulation, fewer than 10% of the original spherical harmonic components were sufficient to recover the signal with nearly unchanged prediction accuracy. What are the implications of the main findings? Gravity field models can be made much more compact without substantial loss of accuracy, which supports more efficient model storage, transmission, and subsequent uncertainty analysis. The study provides a statistically consistent framework for sparse SH modeling, including reference-model handling, objective regularization-parameter selection, and covariance approximation for retained coefficients. Gravity field modeling with spherical harmonics is a fundamental task in physical geodesy. In conventional solutions, all spherical harmonic (SH) components below a prescribed maximum degree and order are typically retained, even though some components may contribute little to the final model. This study investigates SH component selection in gravity field modeling using sparse regularization, specifically the Lasso and adaptive Lasso. A statistical strategy is introduced for incorporating a reference global gravity model by accounting for its variance-covariance information. The resulting L1-norm-regularized estimation problem is solved with an efficient gradient-based algorithm, and the regularization parameter is selected using tailored criteria, including generalized cross-validation and the corrected Akaike information criterion. In addition, an approximate variance-covariance matrix of the estimated parameters is derived analytically from a Bayesian perspective, showing that only the non-zero coefficients contribute to the non-zero covariance structure. Closed-loop simulations based on EGM2008 show that the proposed method achieves modeling accuracy comparable to that of conventional Tikhonov regularization while retaining less than 10% of the SH components. The results demonstrate the feasibility of sparse SH modeling for obtaining compact gravity field representations without substantial loss of accuracy. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Sparse regularization using Lasso and adaptive Lasso can perform spherical harmonic component selection in gravity field modeling while preserving modeling accuracy comparable to conventional Tikhonov regularization. In the closed-loop simulation, fewer than 10% of the original spherical harmonic components were sufficient to recover the signal with nearly unchanged prediction accuracy. What are the implications of the main findings? Gravity field models can be made much more compact without substantial loss of accuracy, which supports more efficient model storage, transmission, and subsequent uncertainty analysis. The study provides a statistically consistent framework for sparse SH modeling, including reference-model handling, objective regularization-parameter selection, and covariance approximation for retained coefficients. Gravity field modeling with spherical harmonics is a fundamental task in physical geodesy. In conventional solutions, all spherical harmonic (SH) components below a prescribed maximum degree and order are typically retained, even though some components may contribute little to the final model. This study investigates SH component selection in gravity field modeling using sparse regularization, specifically the Lasso and adaptive Lasso. A statistical strategy is introduced for incorporating a reference global gravity model by accounting for its variance-covariance information. The resulting L1-norm-regularized estimation problem is solved with an efficient gradient-based algorithm, and the regularization parameter is selected using tailored criteria, including generalized cross-validation and the corrected Akaike information criterion. In addition, an approximate variance-covariance matrix of the estimated parameters is derived analytically from a Bayesian perspective, showing that only the non-zero coefficients contribute to the non-zero covariance structure. Closed-loop simulations based on EGM2008 show that the proposed method achieves modeling accuracy comparable to that of conventional Tikhonov regularization while retaining less than 10% of the SH components. The results demonstrate the feasibility of sparse SH modeling for obtaining compact gravity field representations without substantial loss of accuracy. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101488 |
