V31B-3011
WOVOdat, A Worldwide Volcano Unrest Database, to Improve Eruption Forecasts

Wednesday, 16 December 2015
Poster Hall (Moscone South)
Christina Widiwijayanti1, Fidel Costa1, Nang Thin Zar Win1, Karine Tan1, Christopher G Newhall2, Antonius Ratdomopurbo3 and WOVOdat, (1)Nanyang Technological University, Earth Observatory of Singapore, Singapore, Singapore, (2)Mirisbiris Garden and Nature Center, Santo Domingo, Philippines, (3)Geological Agency of Indonesia, Bandung, Indonesia
Abstract:
WOVOdat is the World Organization of Volcano Observatories’ Database of Volcanic Unrest. An international effort to develop common standards for compiling and storing data on volcanic unrests in a centralized database and freely web-accessible for reference during volcanic crises, comparative studies, and basic research on pre-eruption processes. WOVOdat will be to volcanology as an epidemiological database is to medicine.
Despite the large spectrum of monitoring techniques, the interpretation of monitoring data throughout the evolution of the unrest and making timely forecasts remain the most challenging tasks for volcanologists. The field of eruption forecasting is becoming more quantitative, based on the understanding of the pre-eruptive magmatic processes and dynamic interaction between variables that are at play in a volcanic system. Such forecasts must also acknowledge and express the uncertainties, therefore most of current research in this field focused on the application of event tree analysis to reflect multiple possible scenarios and the probability of each scenario. Such forecasts are critically dependent on comprehensive and authoritative global volcano unrest data sets  the very information currently collected in WOVOdat.
As the database becomes more complete, Boolean searches, side-by-side digital and thus scalable comparisons of unrest, pattern recognition, will generate reliable results. Statistical distribution obtained from WOVOdat can be then used to estimate the probabilities of each scenario after specific patterns of unrest.

We established main web interface for data submission and visualizations, and have now incorporated ~20% of worldwide unrest data into the database, covering more than 100 eruptive episodes. In the upcoming years we will concentrate in acquiring data from volcano observatories develop a robust data query interface, optimizing data mining, and creating tools by which WOVOdat can be used for probabilistic eruption forecasting. The more data in WOVOdat, the more useful it will be.