How do the grain size characteristics of a tephra deposit change over time?

Volcanologists frequently use grain size distributions (GSDs) in tephra layers to infer eruption parameters. However, for long-past eruptions, the accuracy of the reconstruction depends upon the correspondence between the initial tephra deposit and preserved tephra layer on which inferences are based. We ask: how closely does the GSD of a decades-old tephra layer resemble the deposit from which it originated? We addressed this question with a study of the tephra layer produced by the eruption of Mount St Helens, USA, in May 1980. We compared grain size distributions from the fresh, undisturbed tephra with grain size measurements from the surviving tephra layer. We found that the overall grain size characteristics of the tephra layer were similar to the original deposit, and that distinctive features identified by earlier authors had been preserved. However, detailed analysis of our samples showed qualitative differences, specifically a loss of fine material (which we attributed to ‘winnowing’). Understanding how tephra deposits are transformed over time is critical to efforts to reconstruct past eruptions, but inherently difficult to study. We propose long-term, tephra application experiments as a potential way forward.

Acheulian and Tephra from Upland Western Maharashtra, (Deccan Volcanic Province), Peninsular India

The two Early Acheulian sites of Bori and Morgaon from the Deccan Volcanic Province in Upland Maharashtra, India bear some association with an acidic tephra deposit in a fluvial context. Acheulian artefacts in association with tephra were first reported in India from the site of Bori and numerous efforts to date this tephra have since been undertaken. These efforts employed various dating methods and produced a total of 10 absolute dates ranging from the Early Pleistocene with a maximum age of 1.4 ma to the Late Pleistocene with a minimum age of 23.4 ± 2.4 ka. However, field observations and a typo-technological analysis strongly suggests that these Early Acheulian artefacts occur in a semi-primary context and thus contest the Late Pleistocene age of the tephra and re-deposition of the artefacts as argued by some scholars.At Morgaon, the Acheulian artefacts have been recovered from basal fluvial sediments that contain clasts of laterite. These sediments are capped by two low energy clay facies that are separated by a high energy gravel lense. The tephra at Morgaon has been reported from the upper clay facies and has produced absolute dates ranging from the Matuyama period (> 0.78 ma) to the Late Pleistocene (41 ka).After more than two decades of investigation at these sites, the number of absolute dates procured through methods such as ESR, 39Ar-40Ar, U-Th and Palaeo-magnetism though encouraging, are inconclusive. The present communication is therefore an attempt to gauge the nature of palaeo-landscapes that most probably existed during the Early Quaternary. This will be achieved by studying local geomorphological variability between the two sites along with a preliminary analysis of lithic morphology.

Tephra deposit inversion by coupling Tephra2 with the Metropolis-Hastings algorithm: algorithm introduction and demonstration with synthetic datasets

In this work we couple the Metropolis-Hastings algorithm with the volcanic ash transport model Tephra2, and present the coupled algorithm as a new method to estimate the Eruption Source Parameters of volcanic eruptions based on mass per unit area or thickness measurements of tephra fall deposits. Outputs of the algorithm are presented as sample posterior distributions for variables of interest. Basic elements in the algorithm and how to implement it are introduced. Experiments are done with synthetic datasets. These experiments are designed to demonstrate that the algorithm works from different perspectives, and to show how inputs affect its performance. Advantages of the algorithm are that it has the ability to i) incorporate prior knowledge; ii) quantify the uncertainty; iii) capture correlations between variables of interest in the estimated Eruption Source Parameters; and iv) no simplification is assumed in sampling from the posterior probability distribution. A limitation is that some of the inputs need to be specified subjectively, which is designed intentionally such that the full capacity of the Bayes’ rule can be explored by users. How and why inputs of the algorithm affect its performance and how to specify them properly are explained and listed. Correlation between variables of interest in the posterior distributions exists in many of our experiments. They can be well-explained by the physics of tephra transport. We point out that in tephra deposit inversion, caution is needed in attempting to estimate Eruption Source Parameters and wind direction and speed at each elevation level, because this could be unnecessary or would increase the number of variables to be estimated, and these variables could be highly correlated. The algorithm is applied to a mass per unit area dataset of the tephra deposit from the 2011 Kirishima-Shinmoedake eruption. Simulation results from Tephra2 using posterior means from the algorithm are consistent with field observations, suggesting that this approach reliably reconstructs Eruption Source Parameters and wind conditions from deposits.

Tephra deposit inversion by coupling Tephra2 with the Metropolis-Hastings algorithm: algorithm introduction and demonstration with synthetic datasets

In this work we couple the Metropolis-Hastings algorithm with the volcanic ash transport model Tephra2, and present the coupled algorithm as a new method to estimate the Eruption Source Parameters of volcanic eruptions based on mass per unit area or thickness measurements of tephra fall deposits. Outputs of the algorithm are presented as sample posterior distributions for variables of interest. Basic elements in the algorithm and how to implement it are introduced. Experiments are done with synthetic datasets. These experiments are designed to demonstrate that the algorithm works from different perspectives, and to show how inputs affect its performance. Advantages of the algorithm are that it has the ability to i) incorporate prior knowledge; ii) quantify the uncertainty; iii) capture correlations between variables of interest in the estimated Eruption Source Parameters; and iv) no simplification is assumed in sampling from the posterior probability distribution. A limitation is that some of the inputs need to be specified subjectively, which is designed intentionally such that the full capacity of the Bayes’ rule can be explored by users. How and why inputs of the algorithm affect its performance and how to specify them properly are explained and listed. Correlation between variables of interest in the posterior distributions exists in many of our experiments. They can be well-explained by the physics of tephra transport. We point out that in tephra deposit inversion, caution is needed in attempting to estimate Eruption Source Parameters and wind direction and speed at each elevation level, because this could be unnecessary or would increase the number of variables to be estimated, and these variables could be highly correlated. The algorithm is applied to a mass per unit area dataset of the tephra deposit from the 2011 Kirishima-Shinmoedake eruption. Simulation results from Tephra2 using posterior means from the algorithm are consistent with field observations, suggesting that this approach reliably reconstructs Eruption Source Parameters and wind conditions from deposits.

Development of the muographic tephra deposit monitoring system

Measurements of volcanic tephra fallout deposits provide useful information about the magnitude and intensity of explosive volcanic eruptions and potential for remobilization of deposits as dangerous volcanic flows. However, gathering information in the vicinity of erupting craters is extremely dangerous, and moreover, it is often quite difficult to determine deposit thickness proximal to volcanic craters because the thickness of the deposit is too great to easily measure; thus, airborne remote sensing technologies have generally been utilized during the intermission between eruptions. As an alternative tool, a muographic tephra deposit monitoring system was developed in this work. Here we report the performance of this system by applying the muographic data acquired at Sakurajima volcano, Japan as an example. By assuming the average density of the deposit was 2.0 g cm−3, the deposit thicknesses measured with muography were in agreement with the airborne results, indicating that volcanic fallout built up within the upper river basin, showed its potential for monitoring the episodic tephra fallouts even during eruptions.

Tephra Deposit Inversion by Coupling TEPHRA2 With the Metropolis-Hastings Algorithm and Its Implications

In this work we couple the Metropolis-Hastings algorithm with the volcanic ash transport model TEPHRA2, and present the coupled algorithm as a new method to estimate the Eruption Source Parameters of volcanic eruptions based on mass per unit area or thickness measurements of tephra fall deposits. Basic elements in the algorithm and how to implement it are introduced. Experiments are done with synthetic datasets. These experiments are designed to demonstrate that the algorithm works, and to show how inputs affect its performance. Results are presented as sample posterior distribution estimates for variables of interest. Advantages of the algorithm are that it has the ability to i) incorporate prior knowledge; ii) quantify the uncertainty; and iii) capture correlations between variables of interest in the estimated Eruption Source Parameters. A limitation is that some of the inputs need to be specifed subjectively. How and why such inputs affect the performance of the algorithm and how to specify them properly are explained and listed. Correlation between variables of interest are well-explained by the physics of tephra transport. We point out that in tephra deposit inversion, caution is needed in attempting to estimate Eruption Source Parameters, and wind direction and speed at each elevation level, as this increases the number of variables to be estimated. The algorithm is applied to a mass per unit area dataset of the tephra deposit from the 2011 Kirishima-Shinmoedake eruption. Simulation results from TEPHRA2 using posterior means from the algorithm are consistent with field observations, suggesting that this approach reliably reconstructs Eruption Source Parameters and wind conditions from the deposit.

Timescales from mixing to eruption in alkaline volcanism in the Eifel volcanic fields obtained from sanidine and olivine diffusion modelling

<p>Diffusion profiles in sanidine (Ba) and olivine (Mg-Fe, Ca, Mn, and Ni) were used to track recharge events prior to the eruption of the Laacher See volcano, East Eifel volcanic field, western Germany (12.9 ka). Sanidine crystals were analyzed in samples from cumulates and mafic to intermediate phonolites. Olivine crystals occur only in the final mafic eruption products of the compositionally zoned tephra deposit and represent the hybrids of mixing between differentiated phonolite, crystal cumulates, and intruding basanitic magma at the bottom of the magma reservoir. This mixing event is likely related to the eruption triggering event. Additionally, olivine crystals from ten basanitic scoria and maar deposits in the East Eifel and two locations in the West Eifel (Pulvermaar melilith-nephelinite, Meerfelder Maar ol-nephelinite) were analyzed to represent Quaternary parent mafic magmas in Eifel volcanism.</p><p>Olivine from the mafic component that mixed with the Laacher See phonolite are always reversely zoned from cores of variable composition (Fo<sub>83-89</sub>). Zoning of all crystals show trends to a common rim composition (Fo<sub>87.5-89</sub>). Most crystals show additionally a narrow (<10 μm) normally zoned overgrowth at the outermost grain boundary (Fo<sub>86.5-87.5</sub>). Olivine crystals from mafic cones in the East Eifel show similar zoning patterns and core compositions (Fo<sub>80-88</sub>) as those from Laacher See hybrids, but their rims are more variable and always less forsteritic (Fo<sub>83-88</sub>). The lack of olivine rims with >Fo<sub>88</sub> indicates that East Eifel basanites are less primitive than the basanite that intruded into the Laacher See reservoir with olivine rim composition >Fo<sub>89</sub>. However, olivine in samples from the West Eifel nephelinite maar deposits show rim compositions similar to the olivines from Laacher See (Fo<sub>87.5-90</sub>), but are dominantly normal zoned and have high-Fo cores (Fo<sub>88-92</sub>).</p><p>We interpret these observations to indicate that olivine crystals on Laacher See hybrids probably originate from a cumulate or crystal mush with low melt fraction that was disaggregated by the ascending basanite before hybridization. Diffusion modeling of olivine rims indicate a time scale between mixing and eruption of less than 49 days.</p><p>Diffusion times of the sanidine phenocrysts from the intermediate phonolite indicate older recharge events every 1500-3000 yrs that did not result in complete hybridization and eruption. Ba-diffusion times are much shorter for sanidines from the mafic phonolite (4-8 yrs) and the cumulates (months). The reactivation of crystals from cumulates, that can be related to the eruption-triggering recharge event, occurred therefore only months prior to the eruption of Laacher See. These timescales between recharge and eruption are remarkably shorter than the diffusion times calculated for olivine from basanite erupted from scoria cones (up to 500 days).</p>