InSAR and GNSS Time series analysis of Piton de la Fournaise volcano

At Piton de la Fournaise (Reunion Island), during the period 2021-2025, strong ground deformations signals were associated to successive magma intrusions (Peltier et al., 2009) emplaced in April 2021, October 2021, December 2021, September 2022 and July 2023. In this scenario, the signal over noise ratio is high and such signals are also identified on several Sentinel-1 individual interferograms. During inter-eruptive period, baseline measurements for GNSS stations located close to the summit show inter-eruptive signals. Despites the good coverage of the GNSS stations ( 24 on the volcano), the characterization of the spatial extension of inter-eruptive signals remains limited. To overcome the limitation, radar interferometry can bring additional information under the conditions that signal-to-noise ratio is large enough.

The first step of the internship will be to fit GNSS time series during the time period using the trajectory model ITSA, developed at ISTerre to evaluate : i) co-intrusive offsets, ii) pre-eruptive transient related to pressurization of the system and eventually iii) post-eruptive transient related to magma cooling (lava flow or intrusions).

The second step consists to validate the Sentinel-1 InSAR time series considering the GNSS time series as ground truth. To do so, we will systematically compared the co-eruptive offsets and inter-eruptive transient deformation obtained from the InSAR time series at the location of the GNSS stations. Because the presence tropospheric noise impacts the quality of InSAR dataset, we will consider three different time series : i) without atmospheric corrections, ii) corrected using ERA5 weather-based models and iii) corrected using GNSS tropospheric models (Albino et al., 2025).

Finally, the third step will be to model the inter-eruptive signals detected using Bayesian inversion. For the data inversion, we will use GBIS matlab toolbox, a Bayesian approach based on Monte-Carlo Markov-Chain (MCMC), that allows to estimate the optimal values and the level of confidence for each source’s parameter (Bagnardi et al., 2018). Here, the objective will be to evaluate if InSAR time series bring additional constraints for characterizing the transient deformation observed during inter-eruptive periods.

References

Albino, F., Gremion, S., Pinel, V., Bouygues, P., Peltier, A., Beauducel, F., ... & Santoso, A. B. (2025). Benefits of GNSS local observations compared to global weather‐based models for InSAR tropospheric corrections over tropical volcanoes : Case studies of Piton de la Fournaise and Merapi. Journal of Geophysical Research : Solid Earth, 130(4), e2024JB028898.

Albino, F., Biggs, J., Yu, C., & Li, Z. (2020). Automated Methods for Detecting Volcanic Deformation Using Sentinel‐1 InSAR Time Series Illustrated by the 2017–2018 Unrest at Agung, Indonesia. Journal of Geophysical Research : Solid Earth, 125(2), e2019JB017908.

Bagnardi, M., & Hooper, A. (2018). Inversion of surface deformation data for rapid estimates of source parameters and uncertainties : A Bayesian approach. Geochemistry, Geophysics, Geosystems, 19(7), 2194-2211.

Peltier, A., Bachèlery, P., & Staudacher, T. (2009). Magma transport and storage at Piton de La Fournaise (La Réunion) between 1972 and 2007 : A review of geophysical and geochemical data. Journal of Volcanology and Geothermal Research, 184(1-2), 93-108.