Rates and Timescales of Magma Transfer, Storage, Emplacement, and Eruption

Petrelli, Maurizio; Zellmer, Georg F.

doi:10.1002/9781119521143.ch1

Cited by 10 publications

(5 citation statements)

References 359 publications

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“…Our ascent rates are mainly lower than the range of ascent rates (10 -2 -10 2 m/s) of basaltic melts inferred from methods such as isotopic-decay dating, crystal-size distribution analysis, melt-inclusion timekeepers, chemicaldiffusion-based geo-speedometers, thermal modeling of magmatic systems, mechanical modeling of magma ascent and seismicity studies (Petrelli et al 45 and references therein). Our observations relate to the bulk movement of magma whereas other methods, such as melt-inclusion timekeepers and geo-speedometers, relate to the movement of individual crystals which may ascend faster than the bulk magma, potentially explaining why our observations are lower than other estimates of ascent rates.…”

Section: Magma Ascentmentioning

confidence: 75%

Infrasonic gliding reflects a rising magma column at Mount Etna (Italy)

Sciotto

Watson

Cannata

et al. 2022

Sci Rep

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Infrasound is increasing applied as a tool to investigate magma dynamics at active volcanoes, especially at open-vent volcanoes, such as Mt. Etna (Italy), which are prodigious sources of infrasound. Harmonic infrasound signals have been used to constrain crater dimensions and track the movement of magma within the shallow plumbing system. This study interprets the remarkable systematic change in monotonic infrasound signals preceding a lava fountaining episode at Mt. Etna on 20 February 2021. We model the changing tones (0.7 to 3 Hz fundamental frequency) as a rise in the magma column from 172 ± 25 m below the crater rim to 78 ± 8 m over the course of 24 h. The infrasonic gliding disappears approximately 4 h before the onset of lava fountaining as the magma column approaches the flare of the crater and acoustic resonance is no longer supported. The featured 20 February event was just one of 52 lava fountain episodes that occurred at Mt. Etna over the course of 9 months in 2021 and was the only lava fountain episode where dramatic gliding was observed as a subsequent partial collapse of the crater prevented future resonance. The results presented here demonstrate that analysis of infrasonic gliding can be used to track the position of the magma free surface and hence may provide information on the processes taking place within the plumbing system before eruptive activity.

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Section: Magma Ascentmentioning

confidence: 75%

Infrasonic gliding reflects a rising magma column at Mount Etna (Italy)

Sciotto

Watson

Cannata

et al. 2022

Sci Rep

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“…This record often includes the chemistry of different minerals such as olivine, ortopyroxene, clinopyroxene, plagioclase, amphibole, quartz, and garnet (Boschetty et al, 2022). Each of these phases provides clues to decode the dynamics and the evolution of volcanic plumbing systems (Costa et al, 2020;Petrelli and Zellmer, 2020;Ubide et al, 2021). For example, Boschetty et al In the field of metamorphic petrology, Yoshida et al (2018) reported the investigation of metamorphic rocks' evolutive paths during subduction-related metamorphism using Principal Component Analysis, Factor Analysis, and k-means clustering.…”

Section: Clustering and Dimensionality Reductionmentioning

confidence: 99%

Machine Learning in Petrology: State-of-the-Art and Future Perspectives

Petrelli

2024

Preprint

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The present manuscript reports on the state-of-the-art and future perspectives of Machine Learning (ML) in Petrology. To do that, it first introduces the basics of ML, including definitions, core concepts, and applications. Then, it starts reviewing the state-of-the-art of ML in petrology. Established applications mainly concern clustering, dimensionality reduction, classification, and regression. Among them, clustering and dimensionality reduction are particularly valuable for decoding the chemical record stored in igneous and metamorphic phases and to enhance data visualization, respectively. Classification and regression tasks find applications, for example, in petrotectonic discrimination and geothermobarometry, respectively. The main core of the manuscript consists of depicting the next future for ML in petrological applications. I propose a future scenario where ML methods will progressively integrate and support established petrological methods in boosting new findings, possibly providing a paradigm shift. In this framework, the use of multimodal data, data fusion, physics-informed neural networks, and ML-supported numerical simulations, will play a significant role. Also, the use of ML hypotheses formulation and symbolic regression could significantly boost new findings. In the proposed scenario, the main challenges are: a) progressively link machine learning algorithms with the physical and thermodynamic nature of the investigated petrologic processes, b) unblur deep learning algorithms that too often operate as black boxes, c) go ahead in exploring cutting edge tools that rise from researches in Artificial Intelligence, and overall, d) start a collaborative effort among researchers coming from different disciplines in research and teaching.

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“…Experimental observations help us link crystal textures and compositions with geological processes, and thereby reconstruct the structure and dynamics of magma storage regions in ways that can be eventually integrated with geophysical data (Hammer, 2008;Putirka 2008;Gualda et al, 2012;Neave and Putirka 2017;Mollo et al, 2018). The diffusive overprinting of chemical zoning may erase precious records and complicate the integration of plutonic and volcanic archives, but it also enables us to quantify timescales of magma storage and transport, informing volcano monitoring efforts (Kahl et al, 2011;Longpré et al, 2014;Petrone et al, 2016;Rasmussen et al, 2018;Mutch et al, 2019;Costa et al, 2020;Petrelli and Zellmer, 2020).…”

Section: Editorial On the Research Topic Crystal Archives Of Magmatic Processesmentioning

confidence: 99%