Efficient inversion and uncertainty quantification of a tephra fallout model

An efficient and effective inversion and uncertainty quantification approach is proposed for estimating eruption parameters given a data set collected from a tephra deposit. The approach is model independent and here is applied using Tephra2, a code that simulates advective and dispersive tephra transport and deposition. The Levenburg‐Marquardt algorithm is combined with formal Tikhonov and subspace regularization to invert eruption parameters; a linear equation for conditional uncertainty propagation is used to estimate posterior parameter uncertainty. Both the inversion and uncertainty analysis support simultaneous analysis of the full eruption and wind field parameterization. The combined inversion/uncertainty quantification approach is applied to the 1992 eruption of Cerro Negro and the 2011 Kirishima‐Shinmoedake eruption. While eruption mass uncertainty is reduced by inversion against tephra isomass data, considerable uncertainty remains for many eruption and wind field parameters, such as plume height. Supplementing the inversion data set with tephra granulometry data is shown to further reduce the uncertainty of most eruption and wind field parameters. The eruption mass of the 2011 Kirishima‐Shinmoedake eruption is 0.82 × 1010 kg to 2.6 × 1010 kg, with 95% confidence; total eruption mass for the 1992 Cerro Negro eruption is 4.2 × 1010 kg to 7.3 × 1010 kg, with 95% confidence. These results indicate that eruption classification and characterization of eruption parameters can be significantly improved through this uncertainty quantification approach. Key Points An efficient inversion and uncertainty quantification approach for eruption and wind field parameters is demonstrated for two eruption events The worth of tephra grain size data to reduce eruption and wind field parameter uncertainty is quantified Posterior eruption and wind field parameter uncertainty is shown to be eruption and tephra data set specific