A Variational Data Assimilation Framework for Mining Subsidence Reconstruction from Heterogeneous D-InSAR and TLS Observations.
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| Title: | A Variational Data Assimilation Framework for Mining Subsidence Reconstruction from Heterogeneous D-InSAR and TLS Observations. |
|---|---|
| Authors: | Wang, Zijian1 (AUTHOR), Zou, Youfeng1 (AUTHOR) zouyf@hpu.edu.cn, Chai, Huabin1 (AUTHOR), Song, Mingwei1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p2028. 27p. |
| Subjects: | Multisensor data fusion, Mine subsidences, Data assimilation, Sensitivity analysis, Radar interferometry, Remote sensing |
| Abstract: | Highlights: What are the main findings? A multi-source subsidence data fusion method based on a Variational Data Assimilation Framework is proposed. By constructing an objective function with five constraint terms, the D-InSAR-derived boundary observations and TLS-measured central subsidence are integrated into a unified optimization framework. The fusion results achieve an overall RMSE of 0.12 m and an RRMSE of 2.4%. Parameter sensitivity analysis indicates that the smoothness parameter λ s has the most significant influence on fusion accuracy, whereas the background constraint weight has negligible effect over four orders of magnitude. The joint coefficient of variation for all parameter pairs is below 1%, demonstrating the overall robustness of the proposed method. What are the implications of the main findings? The VDAF method effectively resolves the complementary fusion problem between D-InSAR decorrelation in high-gradient deformation areas and the limited spatial coverage of TLS, providing a theoretically rigorous and accuracy-controllable solution for the complete three-dimensional reconstruction of mining-induced subsidence basins. The proposed framework can also be extended to multi-source deformation monitoring in other geohazard scenarios. Comparative analysis shows that D-InSAR completely fails in high-gradient subsidence areas (RMSE up to 3.18 m), while TLS exhibits large errors in certain segments or individual points. By fully exploiting the spatial complementarity of the two data sources, the VDAF method achieves significantly higher accuracy than any single sensor along both observation lines, demonstrating the practical effectiveness of the multi-source fusion strategy in engineering applications. Accurate characterization of mining-induced surface subsidence is essential for safety assessment in mining areas; however, single monitoring techniques have inherent limitations. Spaceborne interferometric synthetic aperture radar (InSAR) provides large-area coverage but suffers from low signal-to-noise ratio in the subsidence center, whereas terrestrial laser scanning offers high accuracy but limited spatial coverage. To achieve physically consistent quantitative fusion, a multi-source subsidence fusion framework based on variational data assimilation is proposed. By constructing an objective function that incorporates a background prior, D-InSAR-derived boundary constraints, TLS observations, spatial smoothness constraints, and gradient penalty terms, multi-source data are integrated into a unified optimization framework. The results show that, compared with RTK observations, the fused subsidence field achieves an RMSE of 0.12 m and an RRMSE of 2.4% approximately. Parameter sensitivity analysis indicates that the smoothing strength has the greatest influence on fusion accuracy, whereas the observation weight and gradient penalty coefficient exhibit relatively wide stable intervals, and the background constraint has a minor effect on the results. Parameter interaction analysis further demonstrates that the coupling between smoothing strength and observation weight is the most significant. The proposed method provides a physically consistent and parameter-controllable framework for multi-source deformation data fusion in mining subsidence monitoring. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 194915161 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Variational Data Assimilation Framework for Mining Subsidence Reconstruction from Heterogeneous D-InSAR and TLS Observations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Zijian%22">Wang, Zijian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zou%2C+Youfeng%22">Zou, Youfeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zouyf@hpu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chai%2C+Huabin%22">Chai, Huabin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Mingwei%22">Song, Mingwei</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p2028. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Mine+subsidences%22">Mine subsidences</searchLink><br /><searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+interferometry%22">Radar interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A multi-source subsidence data fusion method based on a Variational Data Assimilation Framework is proposed. By constructing an objective function with five constraint terms, the D-InSAR-derived boundary observations and TLS-measured central subsidence are integrated into a unified optimization framework. The fusion results achieve an overall RMSE of 0.12 m and an RRMSE of 2.4%. Parameter sensitivity analysis indicates that the smoothness parameter λ s has the most significant influence on fusion accuracy, whereas the background constraint weight has negligible effect over four orders of magnitude. The joint coefficient of variation for all parameter pairs is below 1%, demonstrating the overall robustness of the proposed method. What are the implications of the main findings? The VDAF method effectively resolves the complementary fusion problem between D-InSAR decorrelation in high-gradient deformation areas and the limited spatial coverage of TLS, providing a theoretically rigorous and accuracy-controllable solution for the complete three-dimensional reconstruction of mining-induced subsidence basins. The proposed framework can also be extended to multi-source deformation monitoring in other geohazard scenarios. Comparative analysis shows that D-InSAR completely fails in high-gradient subsidence areas (RMSE up to 3.18 m), while TLS exhibits large errors in certain segments or individual points. By fully exploiting the spatial complementarity of the two data sources, the VDAF method achieves significantly higher accuracy than any single sensor along both observation lines, demonstrating the practical effectiveness of the multi-source fusion strategy in engineering applications. Accurate characterization of mining-induced surface subsidence is essential for safety assessment in mining areas; however, single monitoring techniques have inherent limitations. Spaceborne interferometric synthetic aperture radar (InSAR) provides large-area coverage but suffers from low signal-to-noise ratio in the subsidence center, whereas terrestrial laser scanning offers high accuracy but limited spatial coverage. To achieve physically consistent quantitative fusion, a multi-source subsidence fusion framework based on variational data assimilation is proposed. By constructing an objective function that incorporates a background prior, D-InSAR-derived boundary constraints, TLS observations, spatial smoothness constraints, and gradient penalty terms, multi-source data are integrated into a unified optimization framework. The results show that, compared with RTK observations, the fused subsidence field achieves an RMSE of 0.12 m and an RRMSE of 2.4% approximately. Parameter sensitivity analysis indicates that the smoothing strength has the greatest influence on fusion accuracy, whereas the observation weight and gradient penalty coefficient exhibit relatively wide stable intervals, and the background constraint has a minor effect on the results. Parameter interaction analysis further demonstrates that the coupling between smoothing strength and observation weight is the most significant. The proposed method provides a physically consistent and parameter-controllable framework for multi-source deformation data fusion in mining subsidence monitoring. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18122028 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 2028 Subjects: – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Mine subsidences Type: general – SubjectFull: Data assimilation Type: general – SubjectFull: Sensitivity analysis Type: general – SubjectFull: Radar interferometry Type: general – SubjectFull: Remote sensing Type: general Titles: – TitleFull: A Variational Data Assimilation Framework for Mining Subsidence Reconstruction from Heterogeneous D-InSAR and TLS Observations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Zijian – PersonEntity: Name: NameFull: Zou, Youfeng – PersonEntity: Name: NameFull: Chai, Huabin – PersonEntity: Name: NameFull: Song, Mingwei IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 12 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
