Manta Fabio, A former Structural Geologist graduated in Italy with a great passion for adventure. In 2014 I moved to Singapore where I started a Ph.D. in Geophysics apply to Volcanology at the Earth Observatory of Singapore (EOS). The main subject of my work is modeling ground deformation related to explosive eruptions with the final goal of predicting the size and the intensity of future events. To this end, I apply both numerical approaches such as Finite element Method and analog modeling by making gelatin models in the lab. Thanks to a collaboration developed with Prof Giovanni Occhipinti (IPGP) I started to gain knowledge on GPS data analysis and ionospheric monitoring to introduce alternative methodology for volcanic unrest-detection and tsunami risk estimation. My objective, after my PhD defense, is to continue to work on Ionospheric Seismology as post-doc at the IPGP.
Volcanoes can exhibit a wide range of activities: from effusive eruptions, low-energy bursts, and mild explosive Strombolian eruptions that can cause minor localized effects on human populations, to more severe Plinian eruptions, which are characterized by large emission of ash in the atmosphere with consequent regional to global impact on human life. To monitor the associated risk, volcanologists apply several ground-based and satellites techniques to analyse geophysical signals associated with the mechanisms happening deep inside the earth that can lead to an eruption. These techniques allow, with the appropriate instruments in place, to estimate the time and intensity of coming events and, in the case a large eruption is ongoing, can provide information on ash ejection rates and column heights. Despite technological advancements, many active volcanoes still lack an adequate permanent monitoring network; moreover, harsh climatic conditions can complicate the application of the existing remote sensing techniques. Therefore, there is the need for complementing volcano monitoring with new supportive tools to enhance the current systems. Accordingly, in this thesis we propose two different methods based on volcano tilt observations and ionospheric sounding, respectively for close field and remote sensing applications, to detect and characterize eruptions prior, and during the event. We propose a method to exploit the time series of tilt signals recorded by a single station during Strombolian explosions to forecast the time and magnitude of a coming event. This is achieved by estimating, by mean of the Bayesian statistics and a physics‐based model, the range of the controlling parameters. To validate the proposed model and test its uncertainties we performed analogue experiments in a controlled environment. The analogue approach helped also to shade lights on the interaction between bubbles linked to Strombolian eruptions and the elastic conduit. We finally focused on the development of a new tool that can support remote sensing techniques to detect and assess the intensity of eruptions. We tested whether the analysis of ionospheric Total Electron Content (TEC) can provide additional information to complement the existing monitoring system. To this end, we mined GNSS data recorded during 22 volcanic eruptions to measure the ionospheric TEC perturbation associated with the acoustic-gravity waves generated by volcanic explosions. We evaluated the relationship between a metric related to the energy of atmospheric disturbances, called TEC Intensity Index (TECII), and several well-known metrics obtained by seismology, and satellite remote sensing. The results presented in this thesis support the use of techniques based on the analysis of tilt observations and ionospheric sounding as complementary methods for volcano monitoring. The synergy of these new techniques and the classic ones will augment the possibility of preventing losses of life and mitigating damages by providing useful information for volcano observatories and alert systems.
Oral Examination Committee:
· Associate Professor Fidel Costa, Nanyang Technological University, Singapore (Chairman)
· Assistant Professor Amal Chandran, Nanyang Technological University, Singapore (Oral Examiner)
· Assistant Professor Caroline Bouvet De La Maisonneuve, Nanyang Technological University, Singapore (Oral Examiner)
· Assistant Professor Benoit Taisne, Nanyang Technological University, Singapore