Publications des scientifiques de l'IRD

Boue A., Lesage P., Cortes G., Valette Bernard, Reyes-Davila G. (2015). Real-time eruption forecasting using the material Failure Forecast Method with a Bayesian approach. Journal of Geophysical Research. Solid Earth, 120 (4), p. 2143-2161. ISSN 2169-9313.

Titre du document
Real-time eruption forecasting using the material Failure Forecast Method with a Bayesian approach
Année de publication
2015
Type de document
Article référencé dans le Web of Science WOS:000354563200006
Auteurs
Boue A., Lesage P., Cortes G., Valette Bernard, Reyes-Davila G.
Source
Journal of Geophysical Research. Solid Earth, 2015, 120 (4), p. 2143-2161 ISSN 2169-9313
Many attempts for deterministic forecasting of eruptions and landslides have been performed using the material Failure Forecast Method (FFM). This method consists in adjusting an empirical power law on precursory patterns of seismicity or deformation. Until now, most of the studies have presented hindsight forecasts based on complete time series of precursors and do not evaluate the ability of the method for carrying out real-time forecasting with partial precursory sequences. In this study, we present a rigorous approach of the FFM designed for real-time applications on volcano-seismic precursors. We use a Bayesian approach based on the FFM theory and an automatic classification of seismic events. The probability distributions of the data deduced from the performance of this classification are used as input. As output, it provides the probability of the forecast time at each observation time before the eruption. The spread of the a posteriori probability density function of the prediction time and its stability with respect to the observation time are used as criteria to evaluate the reliability of the forecast. We test the method on precursory accelerations of long-period seismicity prior to vulcanian explosions at Volcan de Colima (Mexico). For explosions preceded by a single phase of seismic acceleration, we obtain accurate and reliable forecasts using approximately 80% of the whole precursory sequence. It is, however, more difficult to apply the method to multiple acceleration patterns.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020] ; Géophysique interne [066]
Description Géographique
MEXIQUE
Localisation
Fonds IRD [F B010064226]
Identifiant IRD
fdi:010064226
Contact