Full Waveform Inversion of Solar Interior Flows

TL;DR

This paper explores the application of Full Waveform Inversion (FWI) to synthetic helioseismic data to recover solar interior flow models, highlighting challenges like slow convergence and depth localization errors.

Contribution

It demonstrates the potential of FWI in helioseismology for flow inversion and discusses the limitations and possible improvements such as probabilistic search methods.

Findings

Misfit reduces with iterations but convergence is slow.

Depth localization errors cause cross talk between flow components.

Probabilistic methods can better identify global minima.

Abstract

The inference of flows of material in the interior of the Sun is a subject of major interest in helioseismology. Here we apply techniques of Full Waveform Inversion (FWI) to synthetic data to test flow inversions. In this idealized setup, we do not model seismic realization noise, training the focus entirely on the problem of whether a chosen supergranulation flow model can be seismically recovered. We define the misfit functional as a sum of L_2 norm deviations in travel times between prediction and observation, as measured using short-distance f and p_1 filtered and large-distance unfiltered $p$ modes. FWI allows for the introduction of measurements of choice and iteratively improving the background model, while monitoring the evolution of the misfit in all desired categories. Although the misfit is seen to uniformly reduce in all categories, convergence to the true model is very slow, possibly because it is trapped in a local minimum. The primary source of error is inaccurate depth localization, which, owing to density stratification, leads to wrong ratios of horizontal and vertical flow velocities (`cross talk'). In the present formulation, the lack of sufficient temporal frequency and spatial resolution makes it difficult to accurately localise flow profiles at depth. We therefore suggest that the most efficient way to discover the global minimum is to perform a probabilistic forward search, involving calculating the misfit associated with a broad range of models (generated, for instance, by a Monte-Carlo algorithm) and locating the deepest minimum. Such techniques possess the added advantage of being able to quantify model uncertainty as well as realization noise (data uncertainty).