A Data Driven Approach to Investigate the Chemical Variability of Clinopyroxenes From the 2014–2015 Holuhraun–Bárdarbunga Eruption (Iceland)

Caricchi, Luca; Petrelli, Maurizio; Bali, Enikő; Sheldrake, Tom; Pioli, Laura; Simpson, Guy

doi:10.3389/feart.2020.00018

Cited by 24 publications

(32 citation statements)

References 42 publications

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“…Additionally, we apply the cpx only ERT barometer to the analyses reported by Caricchi et al (2020) and Geiger et al (2016) for the 2014–2015 Holuhraun eruption (Barðarbunga volcanic system; Figure 11a). Observing Figure 11, it emerges that (1) clinopyroxenes registered crystallization pressures mostly in the range 1–6 kbar; (2) combining all pressure estimates (Figure 11b), the pressure range with the highest probability (i.e., 74%) is 1–3 kbar; and (3) the average of all pressure estimations for the Barðarbunga volcanic system by the ERT barometer is 2.3 ± 1.1 kbar.…”

Section: Discussionmentioning

confidence: 99%

“…Journal of Geophysical Research: Solid Earth Figure 11. Probability density distributions for crystallization pressures obtained by the application of the fully clinopyroxene based ML calibration on the Barðarbunga volcanic system: (a) data by Caracciolo et al (2020) for eruptions in the time range of 100 ka to 1477 AD, Caricchi et al (2020) and Geiger et al (2016) for the 2014-15 Holuhraun eruption; (b) a combination of pressure estimates in panel (a). Figure 12.…”

Section: 1029/2020jb020130mentioning

confidence: 99%

“…Recently, various studies demonstrated the potentials of machine learning (ML) in the solution of petro‐volcanological problems (Bolton et al, 2020; Caricchi et al, 2020; Itano et al, 2020; Li et al, 2020; Petrelli et al, 2017; Petrelli & Perugini, 2016; Ren et al, 2019; Ueki et al, 2020). One common feature for ML models is that they do not need to solve complex problems using an a priori defined conceptual model (e.g., the definition of a thermodynamic reaction) but they can follow a data‐driven approach (Caricchi et al, 2020; Hazen, 2014; Hazen et al, 2019; Morrison et al, 2017) and unravel complexities in large data sets through a so‐called learning process (Shai & Shai, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas

2020

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We introduce a new approach, based on machine learning, to estimate pre-eruptive temperatures and storage depths using clinopyroxene-melt pairs and clinopyroxene-only chemistry. The model is calibrated for magmas of a wide compositional range, it complements existing models, and it can be applied independently of tectonic setting. Additionally, it allows the identification of the main chemical exchange mechanisms occurring in response to pressure and temperature variations on the base of experimental data without a priori assumptions. After the validation process, performances are assessed with test data never used during the training phase. We estimate the uncertainty using the root-mean-square error (RMSE) and the coefficient of determination (R 2). The application of the best performing algorithm (trained in the range 0-40 kbar and 952-1882 K) to clinopyroxene-melt pairs from primitive to extremely differentiated magmas of both subalkaline and alkaline systems returns a RMSE on the order of 2.6 kbar and 40 K for pressure and temperature, respectively. We additionally present a melt-and temperature-independent clinopyroxene barometer in the range 0-40 kbar, characterized by a RMSE of the order of 3 kbar. Tested for tholeiitic compositions in the range 0-10 kbar, the melt-and temperature-independent clinopyroxene barometer has a RMSE of 1.7 kbar. We finally apply the proposed approach to clinopyroxenes from Iceland, providing new, independent, insights about pre-eruptive storage depths of Icelandic volcanoes. The general applicability of this model will promote the comparison between the architecture of plumbing systems across tectonic settings and facilitate the comparison between petrologic and geophysical studies.

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Section: Discussionmentioning

confidence: 99%

Section: 1029/2020jb020130mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas

2020

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“…The implications of our experimentally observed clinopyroxene recrystallisation for natural systems remain unclear, however, partly because it is difficult to gauge how large natural crystals (>100 μm long) would respond from small experimentally produced crystals (≤10 μm long). It is nevertheless plausible that well-documented instances of isobaric clinopyroxene resorption in natural basalts record isobaric changes in magma chemistry as much as changes in magma temperature 12,61 .…”

Section: Discussionmentioning

confidence: 99%

“…10c). Some of the textural complexity observed in natural clinopyroxenes may thus result from mixing between chemically distinct primitive basalts 12,61 .…”

Section: Discussionmentioning

confidence: 99%

Mixing between chemically variable primitive basalts creates and modifies crystal cargoes

Neave¹,

Beckmann²,

Behrens³

et al. 2022

Preprint

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Basaltic crystal cargoes often preserve records of mantle-derived chemical variability that have been erased from their carrier liquids by magma mixing. However, the consequences of mixing between similarly primitive but otherwise chemically variable magmas remain poorly understood despite ubiquitous evidence of chemical variability in primary melt compositions and mixing-induced disequilibrium within erupted crystal cargoes. Here we report observations from magma-magma reaction experiments performed on analogues of primitive Icelandic lavas derived from distinct mantle sources to determine how their crystal cargoes respond to mixing-induced chemical disequilibrium. Chemical variability in our experimental products is controlled dominantly by major element diffusion in the melt that alters phase equilibria and triggers plagioclase resorption within regions that were initially plagioclase saturated. Isothermal mixing between chemically variable basaltic magmas may therefore play important but previously underappreciated roles in creating and modifying crystal cargoes by unlocking plagioclase-rich mushes and driving resorption, (re-)crystallisation and solid-state diffusion.

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Introduction to Machine Learning

Petrelli

2023

Springer Textbooks in Earth Sciences, Geography and Environment

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A Data Driven Approach to Investigate the Chemical Variability of Clinopyroxenes From the 2014–2015 Holuhraun–Bárdarbunga Eruption (Iceland)

Cited by 24 publications

References 42 publications

Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas

Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas

Mixing between chemically variable primitive basalts creates and modifies crystal cargoes

Introduction to Machine Learning

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