Detection, classification, and location of seismovolcanic tremors with multi-component seismic data, example from the Piton de la Fournaise volcano (La Réunion, France)

We apply three different methods based on the analysis of the multi-component seismic data to detect and to classify the seismovolcanic tremors and to locate their sources. We use continuous seismograms recorded during one year by 21 stations at the Piton de la Fournaise volcano (La Reunion, France). The first method allows to detect tremors based on stability in time of the inter-components cross-correlations function. Two other methods based on the simultaneous analysis of the whole network can be used to detect tremors and to locate their sources. The second method consists in performing the 2D back-projection of the inter-stations cross-correlations in order to calculate the network response function. In a third approach, the seismic wavefield is analyzed by calculating the width of the network covariance matrix eigenvalue distribution. Simultaneous analysis of the parameters measured by the three different methods can be used to classify different types of tremors. Our results demonstrate that...