Functioning of Piton de la Fournaise volcano inferred from continuous seismological observations
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Transcript of Functioning of Piton de la Fournaise volcano inferred from continuous seismological observations
Functioning of Piton de la Fournaise volcano inferred from continuous seismological observations
Piton de la Fournaise volcano, La Réunion
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)
2
1
Laboratoire de Géophysique Interne et Tectonophysique
Institut de Physique du Globe de Paris
3Observatoire Volcanologique du Piton de la Fournaise, IPGP, La Réunion, France
Piton de la Fournaise
Sea leve
l
S 21°
E 55°30
Scheme
Topographic map of La Réunion island
Seismic activity and volcanic eruptions
Volcanic eruptions
Cum
ula
tive
sei
smic
ity
days
Explore transient periods from continuous seismological observations.
Waveform recognition
Waveform recognition (work from Elodie Rivemale, Master student)
Calculate correlation coefficient between the master event and the raw seismic signal
Master event : volcano-tectonic event located near the magma reservoir (sea level)
Cor
r. C
oeff
.
Waveform recognition - Results
Waveform recognition
* We identify a constant seismicity rate within transient periods that could berelated with the pressurization of the magma reservoir.
* Comparison of eruptions ER4 and ER5: reactivation of similar dykes
Cum
ula
tive
sei
smic
ityC
umu
lativ
e s
eism
icity
days
Classical Automatic detection
Passive seismic noise monitoring
Seismic noise energy is quite uniform at frequencies [0.1 - 1] Hz – Oceanic originimplies good azimuthal coverage.
Using Seismic noise ?
Green’s function reconstruction from seismic noise
noise sources
receivers
Yang et al. (2007) Shapiro et al. (2005)
Europe South California
PBRZ NCR
Internal structure
3D surface wave tomography using correlations of seismic noise
Brenguier et al., GRL, 2007
Internal structure
solidified dykeseffusive material
3D surface wave tomography using seismic noise
Passive seismic imaging
How the velocity structure evolves along time ?
Time 1 Time 2
time evolution
Passive seismic imaging
Measuring a uniform relative velocity perturbation(Known as Moving Cross-Spectrum Window Method or Coda-wave interferometry)
Synthetic velocity decrease
Data processing
Measuring relative velocity perturbations from observed noise cross-correlations
Results
Testing the method with data from 1999-2000
Brenguier et al., Nature Geoscience, 2008
Time dependent regionalization
Time dependent regionalization
Magmatic intrusive complex
Toward real-time monitoring
Eruption of July 2006
http://www.fournaise.info/eruption2avril07.php
The last eruption of April 2007
Toward real-time monitoring
Toward real-time monitoring
Link between velocity change maxima and emmited volumes
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,
Conclusion
Aknowledgments
Piton de la Fournaise observatory staff and C. Sens-Schoenfelder, L. Stehly, P. Gouédard, P. Roux, G. Poupinet.
Thank you ! – Collaboration with ERI: monitoring Mt Asama volcano
Regionalization procedure
Relative time perturbations for one receiver pair
5 days predated sliding window
5 days predated sliding window
Peltier et al. (2005)
Peltier et al. (2006)
Monitoring ground deformations
Long term variations (few months) Short term variations (few hours)