Surface-wave tomography using SeisLib: a Python package for multiscale seismic imaging

Abstract

Summary To improve our understanding of the Earth’s interior, seismologists often have to deal with enormous amounts of data, requiring automatic tools for their analyses. It is the purpose of this study to present SeisLib, an open-source Python package for multiscale seismic imaging. At present, SeisLib includes routines for carrying out surface-wave tomography tasks based on seismic ambient noise and teleseismic earthquakes. We illustrate here these functionalities, both from the theoretical and algorithmic point of view and by application of our library to seismic data from North America. We first show how SeisLib retrieves surface-wave phase velocities from the ambient noise recorded at pairs of receivers, based on the zero crossings of their normalized cross-spectrum. We then present our implementation of the two-station method, to measure phase velocities from pairs of receivers approximately lying on the same great-circle path as the epicentre of distant earthquakes. We apply these methods to calculate dispersion curves across the conterminous United States, using continuous seismograms from the transportable component of USArray and earthquake recordings from the permanent networks. Overall, we measure 144 272 ambient-noise and 2055 earthquake-based dispersion curves, that we invert for Rayleigh-wave phase-velocity maps. To map the lateral variations in surface-wave velocity, SeisLib exploits a least-squares inversion algorithm based on ray theory. Our implementation supports both equal-area and adaptive parametrizations, with the latter allowing for a finer resolution in the areas characterized by high density of measurements. In the broad period range 4–100 s, the retrieved velocity maps of North America are highly correlated (on average, 96 per cent) and present very small average differences (0.14 ± 0.1 per cent) with those reported in the literature. This points to the robustness of our algorithms. We also produce a global phase-velocity map at the period of 40 s, combining our dispersion measurements with those collected at global scale in previous studies. This allows us to demonstrate the reliability and optimized computational speed of SeisLib, even in presence of very large seismic inverse problems and strong variability in the data coverage. The last part of the manuscript deals with the attenuation of Rayleigh waves, which can be estimated through SeisLib based on the seismic ambient noise recorded at dense arrays of receivers. We apply our algorithm to produce an attenuation map of the United States at the period of 4 s, which we find consistent with the relevant literature.