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

Abstract

Abstract 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.

Keywords

• Tephra2
• Metropolis-Hasings algorithm
• Markov Chain Monte Carlo methods
• Tephra inversion
• Bayesian inversion