<p>The volcanic long-period seismicity, composed of long-period events and volcanic tremors, constitutes an important attribute of volcanic unrest. Its detection and characterization is therefore a key aspect of volcano monitoring. In the present work, a method based on the seismic network covariance matrix, the equivalent in the frequency domain of the cross-correlation matrix, is used to automatically detect and locate long-period events of the Teide volcano on the island of Tenerife (Canary Islands, Spain). The method is based on the analysis of eigenvalues and eigenvectors of the network covariance matrix.</p><p>Long-period events are detected through the time evolution of the width of the network covariance matrix eigenvalues distribution, which is a proxy of the number of sources acting in the wavefield. Each detected long-period event is then located using the moveout information of the corresponding first eigenvector. Three years of seismic data (from 2017 to 2019) continuously recorded by the Red S&#237;smica Canaria (C7), a permanent monitoring network composed of 17 broadband stations operated by the Instituto Volcanol&#243;gico de Canarias (INVOLCAN), are analysed. The obtained locations are compared with potential locations from INVOLCAN&#8217;s catalog, obtained by a standard approach based on manual phases picking.</p>
Classification of Covariance Matrix Eigenvalues in Polarimetric SAR for Environmental Monitoring Applications