Rise from beneath: understanding volatile degassing in distinct volcanic systems through modelling
Volcanic eruptions are among Earth's most perilous and influential natural occurrences. However, volcanic activity remains difficult to predict and, by extension, prepare against. Volcanism and its associated hazards are heavily influenced by the behavior of volatile compounds, such as carbon d...
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Format: | Final Year Project |
Language: | English |
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Nanyang Technological University
2024
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Online Access: | https://hdl.handle.net/10356/174814 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Volcanic eruptions are among Earth's most perilous and influential natural occurrences. However, volcanic activity remains difficult to predict and, by extension, prepare against. Volcanism and its associated hazards are heavily influenced by the behavior of volatile compounds, such as carbon dioxide (CO2), water vapor (H2O), and sulfur (S), during degassing. Volcanic degassing is the process where these volatiles escape from the magmatic system into the atmosphere, significantly guiding eruption dynamics and are key to understand in improving future volcanic prediction models. Despite their importance, volcanic degassing is still poorly understood, with existing models producing conflicting results. This paper thus examines novel and lesser-studied volcanic degassing models, Sulfur_X and D-Compress, and their efficacy in explaining volatile behavior within various volcanic systems, particularly in subduction zones and mantle hot spots. Focusing on historical eruptions of Mt. Pinatubo (arc) and Piton de la Fournaise (hot spot), we analyze the volatiles' behavior using an array of published and experimental data, with an emphasis on magma ascent phases. By simulating and comparing these findings with actual eruption conditions, we aim to provide insights into the factors influencing volatile degassing during these volcanic eruptions, while validating and testing the applicability of various existing models and data sets. |
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