The SAFARI project: An Artificial Intelligence-based strategy for volcano hazard monItoring from space

Identifying the observable signals that warn against volcanic unrest and impending eruptions is one of the greatest challenges in the management of natural disasters. In this regard, satellite data has become a strong focus of global interest, offering abundant datasets from multi-missions and valuable tools to study Earth and improve physical models. The SAFARI project aims at developing a comprehensive space-based strategy for next-generation quantitative volcano hazard monitoring integrating the most recent satellite imagery capabilities and the relative products with the newest technologies mainly in the field of Machine Learning (ML) and Soft Computing. The main objectives of SAFARI include: (i) following the manifestations of unrests and impending eruptions, as well as (ii) forecasting the areas potentially threatened by volcanic products through eruptive scenarios. For this purpose, SAFARI intends to characterize the state of volcanic activity (quiet, unrest and eruptive phases) by taking advantage of a variety of satellite data, including active and passive sensors ranging from optical to microwave frequencies, and to extract quantitative satellite-derived input parameters to physical models for rapid and accurate scenario forecasting during eruptions.  Well-established products from space-based volcano monitoring such as: (i) volcanic radiative power, (ii) surface displacement and (iii) volcanic gas emission (e.g., SO2, BrO) time series are processed jointly and supported by less frequently used but still informative time series such as (iv) ground skin temperature of the volcanic edifices, (v) change detection time series, (vi) time-varying volcanic ash indices, (vii) ash top height time series, (viii) gravity field variation and also (ix) time varying indices giving information about deformation phases of the volcanic edifice (i.e., inflation/deflation) as well as (x) crucial parameters related to the volcanic source (e.g., depth, volume variation) by using data assimilation to deformation models. SAFARI merges and assembles the latest developments from different INGV teams, in a way to analyze Earth observation (EO) data with a retrospective and multi-disciplinary approach, employing traditional statistical or numerical analysis, latest generation Graphic Processing Units (GPUs) architectures and newer and more sophisticated ML algorithms to classify time series, detect anomalies, and predict or estimate significant parameter values.  The methodologies in SAFARI are developed and verified at four active volcanoes worldwide: Etna and Vulcano (Italy), continuously monitored by dense ground based networks managed by INGV, which will provide a first controlled experiment, and Nyiragongo (D.R. Congo) and Sangay (Ecuador), characterized by high volcanic hazard but with modest permanent monitoring networks, where satellite remote sensing is a key monitoring tool. The results of the SAFARI project and its underlying data source and methodologies, as well as the potential of the whole integrated processing chain, aim at becoming an effective tool for volcanic hazard analysis and impact quantification never used to date in volcanology, improving safety and reducing risk associated to eruptive events worldwide.