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Decoding Volcanic Plume Dilution: A Data-Driven Approach to Magmatic Gas Signal Recovery

João Lages1, Luciano Curcio1, Marcello Bitetto1, Angelo Vitale1, and Alessandro Aiuppa1

Abstract

A substantial amount of information on the chemical composition of volcanic plumes has been derived from the MultiGAS technique. This method provides high-frequency (1 Hz) and near-real-time measurements, enabling the systematic detection of precursor cyclic changes in the volcanic gas CO2/SO2 ratio before paroxysmal eruptions (e.g., at Villarrica, Stromboli, and Etna). However, near-vent deployments are often hindered by hazards such as ballistics, fountaining ejecta, and ash fallout. As the distance between the instrument and the source increases, the volcanic gas signal becomes increasingly diluted in the atmosphere, making it more challenging to resolve. We developed a Python-based routine to model CO2 and SO2 concentration time series. Our findings indicate that high-concentration data (≥ 20 ppm SO2) can be effectively modeled using simple monotonic decreasing functions, where CO2/SO2 ratios decrease proportionally with increasing SO2. From this, two points are identified: (i) an inflection point (x1), where variations in CO2/SO2 ratios significantly decrease and mixing of volcanic/atmospheric gases is attenuated; and (ii) a second threshold (x2) beyond which variations in CO2/SO2 ratios become statistically insignificant. Preliminary results indicate that this modeling approach can be applied to low-concentration data, where CO2/SO2 ratios exhibit similar decreasing trends. Using the program-selected models, we extrapolate the mathematical trends to determine x1 and x2, enabling systematic extraction of the magmatic gas signal while mitigating atmospheric dilution effects. This approach has the potential to enhance the reliability of the MultiGAS as an effective volcano monitoring tool, particularly at volcanoes where hazardous activity precludes instrument deployment near the source.