Detection, Classification, and Location of Seismovolcanic Signals with Multicomponent Seismic Data: Example from the Piton de La Fournaise Volcano (La Réunion, France)

Researchers developed three innovative methods to detect, classify, and locate seismovolcanic signals using multicomponent seismic data from the Piton de La Fournaise volcano. These methods efficiently identified tremors linked to the 2010 eruptions and preceding seismic swarms, revealing distinct regions of volcanic activity close to the eruptive site at shallow depths and deeper seismic crises near sea level under the summit crater. The study demonstrates that network-based approaches, particularly those analyzing covariance matrix distributions and backprojecting interstation cross-correlations, significantly enhance the detection and localization of volcanic earthquakes.

Journeau, C., Shapiro, N. M., Seydoux, L., Soubestre, J., Ferrazzini, V., & Peltier, A. (2020). Detection, Classification, and Location of Seismovolcanic Signals with Multicomponent Seismic Data: Example from the Piton de La Fournaise Volcano (La Réunion, France). Journal of Geophysical Research: Solid Earth, 125(8). Portico. https://doi.org/10.1029/2019jb019333