Efficient inversion and uncertainty quantification of a tephra fallout model

Abstract

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.

Distal Enhanced Sedimentation From Volcanic Plumes: Insights From the Secondary Mass Maxima in the 1992 Mount Spurr Fallout Deposits

“…We finally stress the value of combining particle transport modeling with detailed deposit observations, including not only mass depositional patterns on the ground but also detailed studies of grain shape, size, density, and, importantly, direct measurement of Vt s. Finally, we emphasize that including grain size—and not solely the mass—model evaluation of fallout deposits is necessary to fully assess the quality of the sedimentation scheme. Recent modeling work has shown that such approach reduces the uncertainty on the simulation results (White et al, ).…”

“…Finally, we emphasize that including grain size-and not solely the mass-model evaluation of Journal of Geophysical Research: Solid Earth 10.1002/2017JB014412 fallout deposits is necessary to fully assess the quality of the sedimentation scheme. Recent modeling work has shown that such approach reduces the uncertainty on the simulation results (White et al, 2017).…”

“…The complex 3-D wind field is simulated at the mesoscale with the National Center for Atmospheric Research (NCAR) Clark-Hall cloud-scale model (Clark et al, 1996). This model has been used to simulate fire events (Coen, 2013), aircraft-damaging turbulence phenomena (Clark et al, 2000), rainfall development (Thielen et al, 2000), and microphysical processes accompanying cloud seeding (Wobrock et al, 2001). The meteorological model is nonhydrostatic and uses the an-elastic form of the continuity equation.…”

“…New field and experimental evidence demonstrates that the sedimentation mechanisms controlling plume depletion can be complex. In particular, the fall of individual particles through the atmosphere at their terminal velocity only partially describes the settling behavior of tephra (e.g., Del Bello et al, 2017), while additional collective sedimentation processes such as aggregation (e.g., Bagheri et al, 2016;Taddeucci et al, 2011) and en masse collapse of gravitational instabilities (e.g., Carazzo & Jellinek, 2012, 2013Manzella et al, 2015) can be significant.…”

A new method to identify the source vent location of tephra fall deposits: development, testing, and application to key Quaternary eruptions of Western North America

“…However, the variable data quality of different tephra deposits makes it hard to come up with an universal solution. A generalized way of estimating eruption parameters, including the vent position, is to combine inverse methods with tephra transportation models (e.g., Connor and Connor, 2006;Volentik et al, 2010;Bonasia et al, 2010;Klawonn et al, 2012;Burden et al, 2013;Kawabata et al, 2013;White et al, 2017). Although sensitivity analyses have been done to these models (Scollo et al, 2008;Bonadonna et al, 2015;Pouget et al, 2016), their highdimensional parameter space could still introduce uncertainties to the problem.…”