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Mary Grace Bato, JP-Caltech, Source to Surface: Volcano Geodesy & Advances in Eruption Forecasting

Thursday, Jan 28, 2021 12:00 PM
Where: Passcode: 083990
General Public
Geophysics Department

Mary Grace Bato

NASA-JPL , California Institute of Technology, Pasadena

From Source to Surface: Volcano Geodesy and Advances in Real-Time Eruption Forecasting

Today, around 800 million people are living within 100 km of an active volcano. Current practices that lead to successful eruption forecasting are mostly based on empirical pattern recognition, which relies on combining monitoring data with information from global volcanic databases, understanding of a volcano's past behavior (i.e. geological and historical records), and scientific expertise based on experience and local knowledge about the volcano (Segall 2013). The monitoring system in most volcano observatories is generally composed of: 1) gas, 2) seismic, and 3) surface deformation networks, although the contributions of datasets from other instruments (e.g. electrical resistivity, gravity, magnetics, thermal anomaly, and infrasound) are also important. For many volcanoes, surface deformation is a powerful indicator of both their long-term and short-term behaviors. In this seminar, I will talk about the rich information that we can derive from satellite- and/or ground-based volcano deformation data particularly InSAR and/or GNSS timeseries. I will discuss how these geodetic data can be: 1) combined with kinematic or dynamic models and mathematical techniques to estimate, track, and forecast the volcanic system state and 2) used to characterize emplaced volcanic products. I will present some examples showcasing the volcanoes that I have studied in recent years (e.g. Piton de la Fournaise, France; Sierra Negra, Ecuador; Grímsvötn, Iceland) including Taal volcano in the Philippines and its pre- to post-January 2020 eruptive state. Finally, I will discuss the possible direction and the current challenges that we face today in terms of real-time eruption forecasting.  Passcode: 083990                                                    

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