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Transdimensional Mt. Etna Volcano P-Wave Anisotropic Seismic Imaging

Transdimensional inference identifies a class of methods for inverse problems where the number of free parameters is not fixed. In seismic imaging these methods are applied to let the data decide the complexity of the models and how the inferred fields partition the inversion domains. Monte Carlo transdimensional inference is performed implementing the reversible-jump Markov chain Monte Carlo (rjMcMC) algorithm; the nature of Monte Carlo exploration allows the algorithm to be completely non-linear, to explore multiple models with different dimensions and meshes and to investigate the under-determined nature of the tomographic problems. Implementations of this method overcome the main limitations of traditional linearized solvers: the arbitrariness in the selection of the regularization parameters, the linearized iterative approach and in general the collapse of the information behind the solution into a unique inferred model. We present applications of the rjMcMC algorithm to anisotropic seismic imaging of Mt. Etna using P-waves. Mt. Etna is one of the most active and monitored volcanoes in the world, typically investigated under the assumption of isotropic seismic speeds. However, since body waves manifest strong sensitivity to seismic anisotropy, we parametrize a multi-fields inversion to account for the directional dependence in the seismic velocities. Anisotropy increases the ill-condition of the tomographic problems and the consequences of the under-determination are more relevant. When multiple seismic fields - such as speed anomalies and anisotropy - are investigated, the data-sets used may not be able to independently resolve them, resulting in non-independent estimates and corresponding trade-offs. Monte Carlo exploration allows for the evaluation of the robustness of seismic anomalies and anisotropic patterns, as well as the trade-offs between isotropic and anisotropic perturbations, key features for the interpretation of the tomographic models. The approach is completely non-linear, free of any explicit regularization and it keeps the computational time feasible, even for large data-sets.

Session: Translating Seismic Imaging into Geodynamic Understanding [Poster Session]

Type: Poster

Room: Exhibit Hall

Date: 5/1/2024

Presentation Time: 08:00 AM (local time)

Presenting Author: Brandon VanderBeek

Student Presenter: Yes

Additional Authors