Automated analysis of ground deformation time series

23/09/2024

Image European Union, Copernicus Sentinel-2 imagery

Doctoral Candidate: Osmari Aponte

Host institution: GReD

Output type: literature review and summary research topic

Brief Summary of Research

Climate change demands urgent action, leading to innovative solutions to cut greenhouse gas emissions. The COP21 agreement in Paris (2015) set a global target to limit temperature rise from CO2 emissions to 2°C (UNFCCC, 2015). Geological carbon capture and storage is one method to achieve this goal and reduce emissions by 2050 (IEAGHG, 2020). This method, when combined with geothermal energy, offers a promising strategy (Randolph & Saar, 2011). However, the challenge lies in ensuring safety, which requires a thorough understanding and effective monitoring techniques (Zhang et al., 2022).

Recognizing the importance of monitoring underground storage and geothermal activities, space geodetic techniques have been integrated into the multidisciplinary approach to evaluate both natural and anthropogenic surface deformation. GNSS (Global Navigation Satellite System) technologies and interferometric analysis of synthetic aperture radar images (SAR) offer complementary insights into Earth’s surface deformation (Gudmundsson et al., 2002).

Global navigation satellite system (GNSS) is a space geodetic technique that can determine the 3D coordinates of a permanent monitoring site with sub-centimeter accuracy (Benetatos et al., 2020). GNSS-based deformation monitoring offers significant advantages such as real-time and weather-independent monitoring, high precision, and monitoring of dynamic and long-term deformations without needing visibility between points (Shen et al., 2019).

Synthetic Aperture Radar (SAR) techniques offer extensive spatial coverage and high-resolution data (Del Soldato et al., 2021) restricted to a one-dimensional (1D) line of sight (LOS) viewing geometry (Fuhrmann & Garthwaite, 2019). Some applications of SAR technology are SAR Interferometry (InSAR) and Persistent Scatterers Interferometry (PSI), which are valuable monitoring and risk management tools (Areggi et al., 2023; Crosetto et al., 2016; Del Soldato et
al., 2018; Farolfi et al., 2019; Rosi et al., 2014).

The challenge persists in merging SAR’s high-resolution imaging with GNSS’s precise positioning data. While SAR provides accurate Line-Of-Sight (LOS) measurements, it cannot fully capture the magnitude and direction of surface motions (Fuhrmann & Garthwaite, 2019), and the Line-of-Sight (LOS) cannot be directly decomposed into all three coordinate components without introducing bias (Brouwer, 2021). Combining GNSS and InSAR data offers enhanced temporal coverage and improved accuracy in deformation analysis (Del Soldato et al., 2021). Despite advancements, methods for mapping 3D displacements remain a subject of ongoing research, highlighting the need for innovative approaches (Hu et al., 2014; Tondaś et al., 2023).

This research aims to develop an automated tool that integrates GNSS and InSAR data, overcoming the limitations of each method and offering complementary perspectives on ground surface deformation. By developing this tool, we aim to improve our understanding of ground deformation processes with higher spatiotemporal resolution, providing innovative insights for managing geo-energy activities.

Key References

Areggi, G., Pezzo, G., Merryman Boncori, J. P., Anderlini, L., Rossi, G., Serpelloni, E., Zuliani, D., & Bonini, L. (2023). Present-Day Surface Deformation in North-East Italy Using InSAR and GNSS Data. Remote Sensing, 15(6), Article 6. https://doi.org/10.3390/rs15061704

Benetatos, C., Codegone, G., Ferraro, C., Mantegazzi, A., Rocca, V., Tango, G., & Trillo, F. (2020). Multidisciplinary Analysis of Ground Movements: An Underground Gas Storage Case Study. Remote Sensing, 12(21), Article 21. https://doi.org/10.3390/rs12213487

Brouwer, W. (2021). An analysis of the InSAR displacement vector decomposition: InSAR fallacies and the strap-down solution. https://repository.tudelft.nl/islandora/object/uuid%3A9bea6424-c03b-4e0b-95b9-cc6871959f2d

Crosetto, M., Monserrat, O., Cuevas-González, M., Devanthéry, N., & Crippa, B. (2016). Persistent Scatterer Interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 78–89. https://doi.org/10.1016/j.isprsjprs.2015.10.011

Del Soldato, M., Confuorto, P., Bianchini, S., Sbarra, P., & Casagli, N. (2021). Review of Works Combining GNSS and InSAR in Europe. Remote Sensing, 13(9), Article 9. https://doi.org/10.3390/rs13091684

Del Soldato, M., Farolfi, G., Rosi, A., Raspini, F., & Casagli, N. (2018). Subsidence Evolution of the Firenze–Prato–Pistoia Plain (Central Italy) Combining PSI and GNSS Data. Remote Sensing, 10(7), Article 7. https://doi.org/10.3390/rs10071146

Farolfi, G., Del Soldato, M., Bianchini, S., & Casagli, N. (2019). A procedure to use GNSS data to calibrate satellite PSI data for the study of subsidence:an example from the north-western Adriatic coast (Italy). European Journal of Remote Sensing, 52(sup4), 54–63. https://doi.org/10.1080/22797254.2019.1663710

Fuhrmann, T., & Garthwaite, M. C. (2019). Resolving Three-Dimensional Surface Motion with InSAR: Constraints from Multi-Geometry Data Fusion. Remote Sensing, 11(3), Article 3. https://doi.org/10.3390/rs11030241

Gudmundsson, S., Sigmundsson, F., & Carstensen, J. M. (2002). Three-dimensional surface motion maps estimated from combined interferometric synthetic aperture radar and GPS data. Journal of Geophysical Research: Solid Earth, 107(B10), ETG 13-1-ETG 13-14. https://doi.org/10.1029/2001JB000283

Hu, J., Li, Z. W., Ding, X. L., Zhu, J. J., Zhang, L., & Sun, Q. (2014). Resolving three-dimensional surface displacements from InSAR measurements: A review. Earth-Science Reviews, 133, 1–17. https://doi.org/10.1016/j.earscirev.2014.02.005

IEAGHG. (2020). Monitoring and Modelling of CO2 Storage: The Potential for Improving the Cost- Benefit Ratio of Reducing Risk [IEAGHG Technical Report 2020–01]. https://climit.no/app/uploads/sites/4/2020/05/2020-01-Monitoring-and-Modelling-of- CO2-Storage.pdf

Randolph, J. B., & Saar, M. O. (2011). Combining geothermal energy capture with geologic carbon dioxide sequestration. Geophysical Research Letters, 38(10). https://doi.org/10.1029/2011GL047265

Rosi, A., Agostini, A., Tofani, V., & Casagli, N. (2014). A Procedure to Map Subsidence at the Regional Scale Using the Persistent Scatterer Interferometry (PSI) Technique. Remote Sensing, 6(11), Article 11. https://doi.org/10.3390/rs61110510

Shen, N., Chen, L., Liu, J., Wang, L., Tao, T., Wu, D., & Chen, R. (2019). A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring. Remote Sensing, 11(9), Article 9.
https://doi.org/10.3390/rs11091001

Tondaś, D., Ilieva, M., van Leijen, F., van der Marel, H., & Rohm, W. (2023). Kalman filter-based integration of GNSS and InSAR observations for local nonlinear strong deformations. Journal of Geodesy, 97(12), 109. https://doi.org/10.1007/s00190-023-01789-z

UNFCCC. (2015). The Paris Agreement. United Nations Framework Convention on Climate Change (UNFCC). https://unfccc.int/sites/default/files/english_paris_agreement.pdf

Zhang, T., Zhang, W., Yang, R., Gao, H., & Cao, D. (2022). Analysis of Available Conditions for InSAR Surface Deformation Monitoring in CCS Projects. Energies, 15. https://doi.org/10.3390/en15020672