Choice of regularization in adjoint tomography based on two-dimensional synthetic tests

Abstract

We present synthetic tests of 2-D adjoint tomography of surface wave traveltimes obtained by the ambient noise cross-correlation analysis across the Czech Republic. The data coverage may be considered perfect for tomography due to the density of the station distribution. Nevertheless, artefacts in the inferred velocity models arising from the data noise may be still observed when weak regularization (Gaussian smoothing of the misfit gradient) or too many iterations are considered. To examine the effect of the regularization and iteration number on the performance of the tomography in more detail we performed extensive synthetic tests. Instead of the typically used (although criticized) checkerboard test, we propose to carry out the tests with two different target models—simple smooth and complex realistic models. The first test reveals the sensitivity of the result on the data noise, while the second helps to analyse the resolving power of the data set. For various noise and Gaussian smoothing levels, we analysed the convergence towards (or divergence from) the target model with increasing number of iterations. Based on the tests we identified the optimal regularization, which we then employed in the inversion of 16 and 20 s Love-wave group traveltimes.