Issues with time--distance inversions for supergranular flows

Abstract

Aims. Recent studies showed that time--distance inversions for flows start to be dominated by a random noise at a depth of only a few Mm. It was proposed that the ensemble averaging might be a solution to learn about the structure of the convective flows, e.g., about the depth structure of supergranulation. Methods. Time--distance inversion is applied to the statistical sample of ~$10^4$ supergranules, which allows to regularise weakly about the random-noise term of the inversion cost function and hence to have a much better localisation in space. We compare these inversions at four depths (1.9, 2.9, 4.3, and 6.2 Mm) when using different spatio-temporal filtering schemes in order to gain confidence about these inferences. Results. The flows inferred by using different spatio-temporal filtering schemes are different (even by the sign) even-though the formal averaging kernels and the random-noise levels are very similar. The inverted flows alterates its sign several times with depth. It is suggested that this is due to the inaccuracies in the forward problem that are possibly amplified by the inversion. It is possible that also other time--distance inversions are affected by this issue.