# A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing
## 5 Conclusions
(**a**) \(\mathbf{g}_{zx}\), (**b**) \(\mathbf{g}_{xy}\), (**c**) \(\mathbf{g}_{xz}\) (**d**) \(\mathbf{g}_{yy}\), and (**e**) \(\mathbf{g}_{yz}\) components. This figure corresponds to Figure 8 in the main article, which shows the same components in the north-east coordinate system. Figure S3: Real data over Kauring Test site, Australia. Observed Falcon AGG components transformed to data system, which is aligned with the acquisition geometry of the data. (**a**) \(\mathbf{g}_{xy}\) and (**b**) \(\mathbf{g}_{zw}\) components. This figurecorresponds to Figure 11 in the main article, which shows the same components in the north-east coordinate system. Figure S4: Synthetic Falcon AGG with reduced data size, processed using our method. The residuals and the histograms of the residuals (insets): (**a**) \(\mathbf{g}_{xy}\) and (**b**) \(\mathbf{g}_{uv}\) components (in eotvos). (**c**) Convergence curve, showing the root-mean-square error over iterations. Figure S5: Synthetic Falcon AGG with reduced data size, processed using our method. Predicted Falcon AGG data (**a**) \(\mathbf{g}_{xy}\) and (**b**) \(\mathbf{g}_{uv}\) components (in eotvos). Figure S6: Synthetic Falcon AGG with reduced data size, processed using CGLS method. The residuals and the histograms of the residuals (insets): (**a**) \(\mathbf{g}_{xy}\) and (**b**) \(\mathbf{g}_{uv}\) components (in eotvos). (**c**) Convergence curve, showing the root-mean-square error over iterations. Figure S7: Synthetic Falcon AGG with reduced data size, processed using CGLS method. Predicted Falcon AGG data (**a**) \(\mathbf{g}_{xy}\) and (**b**) \(\mathbf{g}_{uv}\) components (in eotvos). Figure S8: Synthetic FTG with reduced data size, processed using our method. The residuals and the histograms of the residuals (insets): (**a**) \(\mathbf{g}_{xx}\), (**b**) \(\mathbf{g}_{xy}\), (**c**) \(\mathbf{g}_{xz}\) (**d**) \(\mathbf{g}_{yy}\), and (**e**) \(\mathbf{g}_{yz}\) components (in eotvos). (**f**) Convergence curve, showing the root-mean-square error over iterations. Figure S9: Synthetic FTG with reduced data size, processed using our method. Predicted FTG data (**a**) \(\mathbf{g}_{xx}\), (**b**) \(\mathbf{g}_{xy}\), (**c**) \(\mathbf{g}_{xz}\) (**d**) \(\mathbf{g}_{yy}\), and (**e**) \(\mathbf{g}_{yz}\) components (in eotvos). Figure S10: Synthetic FTG with reduced data size, processed using CGLS method. The residuals and the histograms of the residuals (insets): (**a**) \(\mathbf{g}_{xx}\), (**b**) \(\mathbf{g}_{xy}\), (**c**) \(\mathbf{g}_{xz}\) (**d**) \(\mathbf{g}_{yy}\), and (**e**) \(\mathbf{g}_{yz}\) components (in eotvos). Figure S8: Synthetic FTG with reduced data size, processed using CGLS method. Predicted FTG data (**a**) \(\mathbf{g}_{xx}\), (**b**) \(\mathbf{g}_{xy}\), (**c**) \(\mathbf{g}_{xz}\) (**d**) \(\mathbf{g}_{yy}\), and (**e**) \(\mathbf{g}_{yz}\) components (in eotvos). Figure S12: Sensitivity Test 1--Noise Level: 5%. Synthetic noise input data: Six noise-contaminated Falcon AGG datasets each generated using a different pseudo-random Gaussian noise realization. Each row corresponds to one noise realization. The left and right columns display the \(\mathbf{g}_{xy}\) and \(\mathbf{g}_{uv}\) components, respectively. Each panel includes an inset showing the histogram of the additive zero-mean Gaussian noise applied to the corresponding noise-free component. The noise standard deviation was set to 5% of the maximum absolute value of each component. Figure S13. Sensitivity Test 1--Noise Level: 5% Residual analysis and convergence behavior for the six noise-contaminated synthetic Falcon AGG datasets. The noise standard deviation was set to 5% of the maximum absolute value of each component. Figure S15: Sensitivity Test 2--Noise Level: 7%. Synthetic noise input data: Six noise-contaminated Falcon AGG datasets, each generated using a different pseudo-random Gaussian noise realization. Each row corresponds to one noise realization. The left and right columns display the the \(\mathbf{g}_{xy}\) and \(\mathbf{g}_{uv}\) components, respectively.