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.
The main aim of this project is to investigate the crustal and mantle structure beneath the Longmenshan fault zone in China, based on a very dense passive seismology profile. The Longmenshan fault zone hosted the Wenchuan earthquake of May 2008 with a magnitude (Mw) of 7.9 and the Lushan earthquake of June 2013 with a magnitude (Mw) of 6.6. It is planned to mainly use the receiver-function method, to investigate the crustal and mantle structure beneath the Longmenshan fault zone. Waveform data are available from the GEOFON data center, under network code 4O.
This data publication contains (i) a slab model of the Cascadia subduction zone, derived from receiver functions, parameterized as depth to the three interfaces: t (top), c (central) and m (Moho), in NetCDF format; (ii) the station measurements of all parameters in the model in tabular and Raysum model file format; (iii) the raw receiver functions in SAC format; and (iv) auxiliary scripts for loading and plotting the data.
A total of 45,601 individual receiver functions recorded at 298 seismic stations distributed across the Cascadia forearc contributed to the slab model. For each station, 100 s recordings symmetric about the P -wave arrival (i.e. 50 s noise and 50 s signal) of earthquakes with magnitudes between 5.5 and 8, in the distance range between 30 and 100 degree, were downloaded from the Incorporated Research Institutions for Seismology (IRIS) data center, the Northern California Earthquake Data Center (NCEDC), and the Natural Resources Canada Data Center (NRCAN). After quality control, radial and transverse receiver functions were computed through frequency-domain simultaneous deconvolution, with an optimal damping factor found through generalized cross validation.
The continental forearc and subducting slab were parameterized as three layers over a mantle half-space, with the subduction stratigraphy bounding interfaces labeled as t (top), c (central) and m (Moho). Synthetic receiver functions were calculated through ray-theoretical modeling of plane-wave scattering at the model interfaces. The thickness, S -wave velocity (VS) and P - to S -wave velocity ratio (VP/VS) of each layer, as well as the common strike and dip of the bottom two layers and the top of the half space (in total 11 parameters) were optimized simultaneously through a simulated annealing global parameter search scheme. The misfit was defined as the anti-correlation (1 minus the cross-correlation coefficient) between the observed and predicted receiver functions, bandpass filtered between 2 and 20 s period duration.
In total, 171, 143 and 137 quality A nodes were determined to constrain the t, c and m interfaces, respectively. At the trench, 105 nodes at 3 km below the local bathymetry were inserted to constrain the t and c interfaces, and at 6.5 km deeper to constrain the m interface, representing typical sediment and igneous crustal thicknesses. A spline surface was fitted to these nodes to yield margin-wide depth models. The spline coefficients were found using singular value decomposition, with the nominal depth uncertainties supplied as weights. The solution was damped by retaining the 116, 117, and 116 largest singular values for the t, c and m interfaces, respectively, based on analysis of L-curves and the Akaike information criterion.
The data set is the supplemental material to Bloch, W., Bostock, M. G., Audet, P. (2023) A Cascadia Slab Model from Receiver Functions. Geochemistry, Geophysics, Geosystems.