Dynamics of Magma Chamber Replenishment Under Buoyancy and Pressure Forces

Longo, Antonella; Garg, Deepak; Papale, Paolo; Montagna, Chiara Paola

doi:10.1029/2022jb025316

Cited by 4 publications

(2 citation statements)

References 88 publications

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“…Additionally, it is difficult to conceive of a scenario whereby the Current pluton would have intruded from shallower depths (in the northwest) to greater depths (in the southeast). Although magma may migrate along weaknesses in the crust and be emplaced laterally, magma flow is principally upwards, driven by buoyancy and pressure forces (Longo et al 2023 ). Given that the Southeast Anomaly occurs at a depth of ~ 1000 m and the Current–Bridge Zone occurs at a depth of ~ 50 m (Fig.…”

Section: Discussionmentioning

confidence: 99%

Characterizing the supra- and subsolidus processes that generated the Current PGE–Cu–Ni deposit, Thunder Bay North Intrusive Complex, Canada: insights from trace elements and multiple S isotopes of sulfides

Brzozowski

Heggie²,

MacTavish³

et al. 2023

Miner Deposita

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The Current deposit is hosted by serpentinized peridotite that intruded rocks of the Quetico Subprovince in the Midcontinent Rift, and is subdivided into three morphologically distinct regions — the shallow and thin Current–Bridge Zone in the northwest, the deep and thick 437–Southeast Anomaly (SEA) Zone in the southeast, and the thick Beaver–Cloud Zone in the middle. The magma parental to the Current deposit became saturated in sulfide as a result of the addition of external S from at least two sources — a deep source characterized by high Δ33S (< 3‰) values, and a shallow source, potentially the Archean metasedimentary country rocks, characterized by low Δ33S (< 0.3‰). Variations in Δ33S–S/Se–Cu/Pd values indicate that the contamination signatures were largely destroyed by interaction of the sulfide liquid with large volumes of uncontaminated silicate melt. The intrusion crystallized sequentially, with the Current–Bridge Zone crystallizing first, followed by the Beaver–Cloud Zone, and lastly by the 437–SEA Zone. This, along with the elevated Cu/Pd ratios in the 437–SEA Zone, which formed as a result of sulfide segregation during an earlier saturation event, and development of igneous layering in this zone, suggests that it represents the feeder channel to the Current deposit. After the intrusion crystallized, the base-metal sulfide mineralogy was modified by circulation of late-stage hydrothermal fluids, with pyrrhotite and pentlandite being replaced by pyrite and millerite, respectively. This fluid activity mobilized metals and semi-metals, including Fe, Ni, S, Se, Co, Cu, Ag, and As, but did not affect the PGE. This contribution highlights the importance of the interplay between magma dynamics and magmatic–hydrothermal processes in the formation of Ni–Cu–PGE-mineralized deposits.

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Section: Discussionmentioning

confidence: 99%

Characterizing the supra- and subsolidus processes that generated the Current PGE–Cu–Ni deposit, Thunder Bay North Intrusive Complex, Canada: insights from trace elements and multiple S isotopes of sulfides

Brzozowski

Heggie²,

MacTavish³

et al. 2023

Miner Deposita

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show abstract

“…However, as reported by Angione et al (2022) term 'emulator' should be specifically used for methods that provide full probabilistic predictions of simulation behavior, not only approximate results. Natural use of emulators and surrogate is in computational flow dynamic simulations, a scientific tool that found large applications in petrology (Katz, 2008;Gutiérrez and Parada, 2010;Petrelli et al, 2011Parmigiani et al, 2014Parmigiani et al, , 2016Parmigiani et al, , 2017Robertson et al, 2015;Keller and Katz, 2016;Brogi et al, 2022;Longo et al, 2023). As an example, using the output of costly computational flow dynamic simulations, we can train deep ML surrogates or emulators that can predict the outcomes of the modeling.…”

Section: Emulators Surrogates and Providing Boundary Or Driving Condi...mentioning

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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Gas buffering of magma chamber contraction during persistent explosive activity at Mt. Etna volcano

Carbone,

Cannavò,

Montagna

et al. 2023

Commun Earth Environ

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A sequence of more than 20 short-lasting explosive eruptions took place at Mt. Etna volcano, during a 2-month period in 2021. Here we perform a joint analysis of the gravity decrease and ground deflation that accompanied the sequence of eruptions. Results from this joint analysis are cross-checked against the output of a numerical code providing independent geochemical insight on how the density of the magmatic liquid/gas mixture in the source reservoir varies as a function of the pressure. This cross-analysis provides a framework to explain why (i) the bulk volume reduction sensed by the ground deformation data is much lower than the volume of the erupted products and (ii) the observed gravity changes point to a strong mass decrease, incompatible with a pure mechanism of magma withdrawal. We conclude that pressure-driven gas exsolution and expansion compensated the withdrawal of magma, thus buffering the contraction of the source reservoir and leading to the inferred mass decrease.

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Dynamics of Magma Chamber Replenishment Under Buoyancy and Pressure Forces

Abstract: Magmatic systems below active volcanoes can be extremely complex in terms of their shape, physical properties, and evolution (

Cited by 4 publications

References 88 publications

Characterizing the supra- and subsolidus processes that generated the Current PGE–Cu–Ni deposit, Thunder Bay North Intrusive Complex, Canada: insights from trace elements and multiple S isotopes of sulfides

Characterizing the supra- and subsolidus processes that generated the Current PGE–Cu–Ni deposit, Thunder Bay North Intrusive Complex, Canada: insights from trace elements and multiple S isotopes of sulfides

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

Gas buffering of magma chamber contraction during persistent explosive activity at Mt. Etna volcano

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