The data present the intermediate to final results when we introduce a two-step fully Bayesian approach with coupled uncertainty propagation for estimating crustal isotropic and radial anisotropy models using Rayleigh and Love dispersion data along with receiver functions in Sri Lanka.
In the first step, 2D surface wave tomography is used to generate period-wise ambient noise phase velocity maps for Rayleigh and Love waves along with their associated uncertainties. Here we provide the inter-station dispersion data (folder: 2024-003_1_Ke-et-al_interstation_surface_ dispersion_curves; ASCII) for the 2D surface wave tomography process, along with the results of the tomography, including the velocity maps (folder: 2024-003_Ke-et-al_2_velocity_map; ASCII). In addition, the results (folder: 2024-003_3_Ke-et-al_2Dmcmc_inversion_results) are available in MAT format, along with a MATLAB script to allow users to extract the data independently.
In a second step, local surface wave dispersion and model errors are derived from the velocity maps. The surface wave dispersion receiver functions are jointly inverted to obtain the isotropic mean shear wave and radial anisotropy profiles as a function of depth at each station site. The input data (folder: 2024-003_Ke-et-al_4_inv_data; ASCII) of surface dispersion and receiver function for the inversion are presented here, as well as the final result model from the inversion (folder: 2024-003_Ke-et-al_5_model; ASCII and .dat formats).
We study the crustal structure of Sri Lanka by analyzing data from a temporary seismic network deployed in 2016-2017 (Seneviratne et al., 2016) to shed light on the amalgamation process from the geophysical perspective. Rayleigh wave phase dispersion from ambient noise cross-correlation and receiver functions were jointly inverted using a transdimensional Bayesian approach (Bodin et al., 2012, Dreiling & Tilmann, 2019).The dataset provides results from Dreiling et al. (2020) and includes multiple files:(1) the ASCII file 1-results.dat, including the Moho depths and Vp/Vs derived from Bayesian inversion and Hκ-stack grid search, and average crustal Vs and depth of mid-crustal interface from Bayesian inversion and uncertainties for each seismic station, as summarized in Table S2 in the Supporting Information to the manuscript, and(2) the ASCII files *_models.dat (*explicit station name), each includes the final median Vs-depth model beneath each station derived from Bayesian inversion, including Vs standard deviations.