Functioning of Piton de la Fournaise volcano inferred from continuous seismological observations

1. Functioning of Piton de la Fournaise volcano inferred from continuous seismological observations F. Brenguier (1), N. Shapiro (1), M. Campillo (2), V. Ferrazzini (3), Z. Duputel (3), O. Coutant (2), E. Rivemale (1), and A. Nercessian (1) 1 Institut de Physique du Globe de Paris Laboratoire de Géophysique Interne et Tectonophysique 2 3 Observatoire Volcanologique du Piton de la Fournaise, IPGP, La Réunion, France Piton de la Fournaise volcano, La Réunion

2. Piton de la Fournaise Scheme Topographic map of La Réunion island Sea level S 21° E 55°30

3. Seismic activity and volcanic eruptions Explore transient periods from continuous seismological observations. Volcanic eruptions Cumulative seismicity days

4. Waveform recognition Waveform recognition(work from Elodie Rivemale, Master student) Master event : volcano-tectonic event located near the magma reservoir (sea level) Corr. Coeff. Calculate correlation coefficient between the master event and the raw seismic signal

5. Waveform recognition -Results Classical Automatic detection Cumulative seismicity Waveform recognition Cumulative seismicity days * We identify a constant seismicity rate within transient periods that could be related with the pressurization of the magma reservoir. * Comparison of eruptions ER4 and ER5: reactivation of similar dykes

6. Passive seismic noise monitoring Using Seismic noise ? Seismic noise energy is quite uniform at frequencies [0.1 - 1] Hz – Oceanic origin implies good azimuthal coverage.

7. Europe South California Shapiro et al. (2005) Yang et al. (2007) Green’s function reconstruction from seismic noise noise sources PBRZ NCR receivers

8. Internal structure 3D surface wave tomography using correlations of seismic noise

9. Internal structure 3D surface wave tomography using seismic noise solidified dykes effusive material Brenguier et al., GRL, 2007

10. Passive seismic imaging Time 1 Time 2 time evolution How the velocity structure evolves along time ?

11. Passive seismic imaging Measuring a uniform relative velocity perturbation (Known as Moving Cross-Spectrum Window Method or Coda-wave interferometry) Synthetic velocity decrease

12. Data processing Measuring relative velocity perturbations from observed noise cross-correlations

13. Results Testing the method with data from 1999-2000 Brenguier et al., Nature Geoscience, 2008

14. Time dependent regionalization

15. Magmatic intrusive complex Time dependent regionalization

16. Toward real-time monitoring Eruption of July 2006

17. Toward real-time monitoring The last eruption of April 2007 http://www.fournaise.info/eruption2avril07.php

18. Toward real-time monitoring Link between velocity change maxima and emmited volumes

19. Conclusion Conclusions • We measure relative velocity changes with a precision of 0.02 %, • These changes are linked to dilatation induced by stress changes, • We identified precursors to the volcanic eruptions. Prospects • We are looking to localize the velocity changes at depth (4D tomography), • Increase the time resolution, Aknowledgments Piton de la Fournaise observatory staff andC. Sens-Schoenfelder, L. Stehly, P. Gouédard, P. Roux, G. Poupinet.

20. Thank you ! – Collaboration with ERI: monitoring Mt Asama volcano

21. Regionalization procedure Relative time perturbations for one receiver pair

22. 5 days predated sliding window

23. 5 days predated sliding window

25. Monitoring ground deformations Long term variations (few months) Short term variations (few hours) Peltier et al. (2006) Peltier et al. (2005)