ALBERT

Description: To better understand the shallow structure of El Hierro island, we determined a 3D S-wave velocity model using Ambient Noise Tomography (ANT). We exploited a dataset recorded by 21 broadband seismic stations deployed at El Hierro island in two surveys realized in 2015 and 2021. This dataset allowed us to obtain empirical Green’s functions by cross-correlating seismic ambient noise signals and retrieving 105 dispersion curves using the frequency-time analysis (FTAN). Then we obtained 2-D Rayleigh wave group velocity maps for periods between 0.6 s and 2.6 s through a non-linear multiscale inversion (Cabrera-Pérez et al, 2021). Finally, we performed depth inversion through a Bayesian transdimensional approach to obtain a 3-D S-wave velocity model. The obtained ANT model is merged with a local earthquake tomography model (García-Yeguas et al, 2014). Our study highlights six relevant seismic velocity anomalies. We observed the presence of three high-velocity zones located in the eastern, western and northern parts of the island, which could be related to intrusive bodies possibly associated with the formation of El Hierro island. We also observed three low-velocity anomalies in the northern and southern parts. The anomaly in the North of the island could be related to loose deposits generated by the El Golfo valley megalandslide. The anomalies in the South could be related to porous and highly fractured materials produced during the more recent volcanic episodes.

Description: The joint analysis of multiparametric datasets in geophysics and, in general, in geosciences is often challenging due to the highly different measurement types. During the last decades, data mining techniques have been subject to intense development, which allows the detection and characterizing of “hidden patterns” within complex datasets. One of the most successful and widely used techniques is the Independent Component Analysis (ICA) which allows for identifying spatio-temporal patterns related to independent sources from dense geospatial datasets. In this work, we apply an extension of the ICA named Independent Vector Analysis (IVA) to analyze a multiparametric dataset of spontaneous potential, CO2 and H2S flux and thermal gradient measurement realized in the crater of Mt. Teide (Tenerife, Canary Islands), from 2020 to 2023. While ICA allows studying spatio-temporal patterns of a single quantity, IVA allows dealing with means multiparametric measurements realized on a single point, which means using vector data instead of a simple scalar. The relationship between spontaneous potential and gas emission is well known and testified by numerous case studies. In this work, however, we exploit for the first time this quantitative approach to separate and characterize endogenous and external factors in this dataset. The approach we propose in this work has a broader application to repeated multiparametric geophysical surveys and in combining geophysical datasets with other kinds of data.

Description: In recent years the use of Distributed Acoustic Sensing (DAS) in seismology is gaining extensive usage in different applications. A High-Fidelity DAS system (HDAS) was deployed during the 2021 Tajogaite eruption on Cumbre Volcano (La Palma, Canary Islands), allowing the recording of most of the syn-eruptive and post-eruptive seismicity. The eruption lasted from Sep. 19th until Dec. 13th of 2021. The HDAS was installed on 19th Oct. 2021 and is still operating. The HDAS was installed around 10 km from the eruptive vent and was connected to a submarine fibre optic cable directed toward Tenerife Island. Since then, the HDAS has been recording seismic with a temporal sampling rate of 100 Hz and a spatial sampling rate of 10m for a total length of 30 (first phase) and 50 km using Raman Amplification (last period). The HDAS recorded thousands of local earthquakes as well as regional and teleseismic events. It was revealed to be an excellent tool for volcanic monitoring, allowing a better location of deeper events which location was made difficult by the small aperture of the seismic network of La Palma. The HDAS was also able to record the low-frequency (〈1 Hz) component of the volcanic tremor up to a distance of tens of kilometres from the volcano. We show how, using array-like techniques, it is possible to identify and separate the volcanic tremor signals from the oceanic ambient noise. In this work, we demonstrate the effectiveness of using DAS as a real-time volcano monitoring tool.

Description: The 2021 Tajogaite eruption was characterized by short precursors, lasting only eight days. The seismicity started on Sep. 11th with a westward and upward migration of hypocenters. Permanent GNSS stations started recording deformation of the island's western side, which reached more than 15 cm just before the eruption. After the eruption's onset, which occurred on September 19th, the deformation increased, reaching a maximum on September 22nd and later showing a nearly steady deflation trend in the following months. To better understand the dynamics of the eruption, we exploited a joint dataset of GNSS and Sentinel-SBAS time series along both ascending and descending orbits. To obtain the geometry of the causative source of the ground deformation, we combined the result of a preliminary non-linear inversion and the precise distribution of the seismicity. The resulting geometry is that of a twisted dike bending eastward. We realized a spatio-temporal inverse modelling to obtain the evolution of the causative source of the ground deformation. The inverse modelling has been realized using a 3D finite-element approach considering the island's topography. The main results we obtained are that we observe a correspondence between the magmatic intrusion and pre-eruptive seismicity; the ascent of the magma occurred along two different branches; the rheology of a previously identified ductile layer strongly affected the magma propagation process. Finally, we evidence an early shallow deformation which we interpret as the effect of ascending hydrothermal fluids. Our findings highlight the need for advanced modelling to understand pre-eruptive processes in basaltic volcanoes.