Rapid crystallization during recycling of basaltic andesite tephra:Timescales determined by reheating experiments

Microcrystalline inclusions within microlite-poor matrix are surprisingly common in low intensity eruptions around the world, yet their origin is poorly understood. Inclusions are commonly interpreted as evidence of crystallization along conduit margins. Alternatively, these clasts may be recycled from low level eruptions where they recrystallize by heating within the vent. We conducted a series of experiments heating basaltic andesite lapilli from temperatures below the glass transition (~690 °C) to above inferred eruption temperatures (>1150 °C) for durations of 2 to >60 minutes. At 690 °C < T < 800 °C, crystallization is evident after heating for ~20 minutes; at T > 800 °C, crystallization occurs in <5 minutes. At T ≥ 900 °C, all samples recrystallize extensively in 2–10 minutes, with pyroxenes, Fe-oxides, and plagioclase. Experimental crystallization textures closely resemble those observed in natural microcrystalline inclusions. Comparison of inclusion textures in lapilli from the active submarine volcano NW Rota-1, Mariana arc and subaerial volcano Stromboli suggest that characteristic signatures of clast recycling are different in the two environments. Specifically, chlorine assimilation provides key evidence of recycling in submarine samples, while bands of oxides bordering microcrystalline inclusions are unique to subaerial environments. Correct identification of recycling at basaltic vents will improve (lower) estimates of mass eruption rate and help to refine interpretations of eruption dynamics

Looking backward and forward: volcanology in the years 2000, 2010, 2020, and beyond

Figuring out how volcanoes work is one of the geoscience’s most complex puzzles. Clues of all sizes, shapes, and colors are scattered across every continent, the bottom of the ocean, in the atmosphere, and on the surfaces of other planets. Generations of geologists, geophysicists, geodesists, and geochemists have used field observations, laboratory measurements, and theory to fill gaps left by their predecessors. Yet critical uncertainties remain. Why do eruptions begin? What determines their intensity? What controls their frequency and style of activity? What causes them to end? These unsolved issues leave society increasingly vulnerable to volcanic disruptions. Hundreds of published papers supplemented by dozens of review articles and compendia like the Encyclopedia of VolcanologyFootnote1 each offer a snapshot showing steps volcanologists have taken to piece together answers to these fundamental questions

Cashman receives 2006 N. L. Bowen Award

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95516/1/eost15852.pd

Optimising shape analysis to quantify volcanic ash morphology

AbstractAccurate measurements of volcanic ash morphology are critical to improving both our understanding of fragmentation processes and our ability to predict particle behaviour. In this study, we present new ways to choose and apply shape parameters relevant to volcanic ash characterisation. First, we compare shape measurements from different imaging techniques, including cross-sectional (2-D) and projected area images, and discuss their respective applications. We then focus on specific information that can be obtained from shape analysis of 2-D images. Using cluster analysis as an unbiased method to identify key controls on particle morphology, we find that four shape parameters – solidity, convexity, axial ratio, and form factor – can effectively account for the morphological variance within most ash samples. Importantly, these parameters are scaled to values between 0 and 1, and therefore contribute evenly to discrimination diagrams. In particular, co-variation in convexity and solidity can be used to distinguish different juvenile ash components based on characteristic bubble properties. By reducing observations of natural samples to simplified ash geometries, we quantify morphological changes associated with variations in the relative size and shape of bubbles and particles. Using this relationship, we assess the potential application of size-dependent shape analysis for inferring the underlying bubble size distribution, and thus the pre-fragmentation conditions. Finally, we show that particle shape analysis that includes the full range of available grain sizes can contribute not only measurements of particle size and shape, but also information on size-dependent densities

Remote Characterization of Dominant Wavelengths From Surface Folding on Lava Flows Using Lidar and Discrete Fourier Transform Analyses

Surface folding is common in lava flows of all compositions and is believed to be due to changes in viscosity and flow velocity between the cooling crust and the more fluid flow interior. However, our understanding of the relationship between surface folding and flow rheology is incomplete. In this study we analyze digital terrain models of eight lava flows ranging in composition from basaltic andesite to rhyolite using a discrete Fourier transform analysis to quantitatively determine dominant surface fold wavelengths. Our discrete Fourier transform analyses show that each lava flow has multiple fold generations and that dominant wavelengths are more closely related to calculated effective viscosity than to lava composition. At our Oregon sites, average dominant wavelengths generally increase with viscosity (r2=0.68), and the correlation improves (r2=0.87) when expanded by including previously measured fold wavelengths and viscosities from the global database. However, there are a few exceptions to this positive trend where a few lava flows have lower or higher than expected dominant fold wavelengths, which we infer are due to secondary factors such as differences in eruption conditions (eruption rate, temperature, etc.). Additionally, over a 5 order of magnitude range in viscosity, there is significant overlap between the ranges of fold wavelengths, particularly from 10 to 20m, for lavas from basaltic andesite to rhyolite, making it difficult to determine a numeric correlation between surface folds and lava rheology that would allow remote characterization of lava