Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion

TL;DR

This paper presents a novel method for computing exact anelastic sensitivity kernels in the time domain efficiently, avoiding instabilities and reducing computational costs, with validation confirming accuracy and importance of full attenuation modeling.

Contribution

The authors introduce a parsimonious storage technique for exact anelastic sensitivity kernels that improves stability and efficiency in adjoint tomography and full waveform inversion.

Findings

The method is exact and validated against full forward calculations.

It reduces computational cost by a factor of 4/3 compared to partial dispersion methods.

Including full attenuation significantly affects sensitivity kernel results.

Abstract

We introduce a technique to compute exact anelastic sensitivity kernels in the time domain using parsimonious disk storage. The method is based on a reordering of the time loop of time-domain forward/adjoint wave propagation solvers combined with the use of a memory buffer. It avoids instabilities that occur when time-reversing dissipative wave propagation simulations. The total number of required time steps is unchanged compared to usual acoustic or elastic approaches. The cost is reduced by a factor of 4/3 compared to the case in which anelasticity is partially accounted for by accommodating the effects of physical dispersion. We validate our technique by performing a test in which we compare the $K_\alpha$ sensitivity kernel to the exact kernel obtained by saving the entire forward calculation. This benchmark confirms that our approach is also exact. We illustrate the importance of including full attenuation in the calculation of sensitivity kernels by showing significant differences with physical-dispersion-only kernels.