Statistical analysis of crystal populations and links to volcano deformation for more robust estimates of magma replenishment volumes

Abstract

Forecasting the timing and size of volcanic eruptions requires a proper interpretation of multiparametric monitoring signals. Studies of the erupted rocks can provide critical information on the processes and volcano plumbing system that is needed to decode the monitoring signals. Here we present the results of a petrological study of plagioclase phenocrysts using a new statistical approach that allows us to estimate the amount of intruded magma before eruption. Our crystal population analysis of the 2006 and 2010 CE Merapi volcano (Indonesia) eruptions shows that ∼60 ± 20 vol% of the 2010 magma was left over from the 2006 magma, and thus ∼40 ± 20 vol% was newly intruded magma. Using the published values of the 2010 erupted magma volume, this corresponds to >8 to 20 (±7) × 106 m3 of new magma. This is a minimum estimate and is similar to the inferred pre-eruptive deformation volume (18 ×106 m3), although given the uncertainties, several million cubic meters of magma intruded in 2010 could still be in the Merapi plumbing system. Our approach could be used at other volcanoes to quantify the volume of intruded magma and thus help in better understanding the unrest signals that anticipate eruptions.

Quantitative chemical mapping of plagioclase as a tool for the interpretation of volcanic stratigraphy: an example from Saint Kitts, Lesser Antilles

“…their areal extent, using transects alone is clearly difficult owing to stereological constraints and under sampling (Probst et al, 2018). Quantifying sample-scale variation has more commonly been approached using back-scattered electron (BSE) maps of thin sections (Cheng et al, 2017;Cheng and Costa, 2019;Humphreys et al, 2013;Zeng et al, 2018), for example by calibrating BSE greyscale images with the punctual determination of anorthite content (Ginibre et al, 2002). Sheldrake and Higgins (2021) presented a method using image segmentation of X-ray maps to classify texturally constrained zones of similar chemical composition.…”

Olivine in picrites from continental flood basalt provinces classified using machine learning

“…Cheng et al, 2020;Costa, 2020;Costa et al, 2020) and the random cut effect in thin sections (e.g. Cheng et al, 2017;Cheng and Costa, 2019). The cardinality measures the number of distinct values.…”

Clocks in Magmatic Rocks

“…Unfortunately, it is not possible to determine the age of each crystal zone precisely enough to determine when they grew or how many are recycled from previous eruptions. Much time and many financial resources are currently used to unravel the time information from different crystal populations using isotope studies (e.g., Davidson et al 2007) or using statistical analysis of major elements from hundreds of crystals (Cheng & Costa 2019). Figure courtesy of D. Ruth.…”

“…variety of crystal populations may be the result of interactions between magmas originating from the lower crust to the shallow subvolcanic reservoirs (e.g., Cashman et al 2017), and much effort and many financial resources are currently used to untangle the various crystal populations, ranges of crystal ages, and their origins (e.g., Cheng & Costa 2019, Davidson et al 2007). This article reviews some recent findings related to the timescales of magmatic systems.…”