Uncertainties, complexities and possible forecasting of Volcán de Colima energy emissions (Mexico, years 2013–2015) based on a fractal reconstruction theorem

• Published in: Nonlinear Processes in Geophysics
• Main Authors: Monterrubio Velasco, Marisol, Lana Pons, Francisco Javier, Arámbula Mendoza, Raúl
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2023
• Subjects: Àrees temàtiques de la UPC::Enginyeria civil::Geologia::Riscos geològics; Volcanic activity prediction; Activitat volcànica > Previsió; Norway; Iceland
• Online Access: http://hdl.handle.net/2117/399129; https://doi.org/10.5194/npg-30-571-2023

The effusive–explosive energy emission process in a volcano is a dynamic and complex physical phenomenon. The importance of quantifying this complexity in terms of the physical and mathematical mechanisms that govern these emissions should be a requirement for deciding to apply a possible forecasting strategy with a sufficient degree of certainty. The complexity of this process is determined in this research by means of the reconstruction theorem and statistical procedures applied to the effusive–explosive volcanic energy emissions corresponding to the activity in the Volcán de Colima (western segment of the Trans-Mexican Volcanic Belt) along the years 2013–2015. The analysis is focused on measuring the degree of persistence or randomness of the series, the degree of predictability of energy emissions, and the quantification of the degree of complexity and “memory loss” of the physical mechanism throughout an episode of volcanic emissions. The results indicate that the analysed time series depict a high degree of persistence and low memory loss, making the mentioned effusive–explosive volcanic emission structure a candidate for successfully applying a forecasting strategy. This research has been supported by the European High-Performance Computing Joint Undertaking (JU) as well as Spain, Italy, Iceland, Germany, Norway, France, Finland and Croatia under grant agreement no.101093038, (ChEESE-CoE). Peer Reviewed Postprint (published version)