2026-05-28T01:46:57Zhttp://doidb.wdc-terra.org/oaip/oaioai:doidb.wdc-terra.org:68332020-05-08T11:05:35ZDOIDBDOIDB.GFZfalse4DOIDB.GFZ
10.5880/GFZ.2.4.2020.001
Dreiling, Jennifer
Jennifer
Dreiling
0000-0001-6882-2076
GFZ German Research Centre for Geosciences, Potsdam, Germany
Tilmann, Frederik
Frederik
Tilmann
0000-0002-7439-8782
GFZ German Research Centre for Geosciences, Potsdam, Germany
Yuan, Xiaohui
Xiaohui
Yuan
0000-0002-2891-1354
GFZ German Research Centre for Geosciences, Potsdam, Germany
Haberland, Christian
Christian
Haberland
0000-0002-2981-7087
GFZ German Research Centre for Geosciences, Potsdam, Germany
Seneviratne, S.W. Mahinda
S.W. Mahinda
Seneviratne
Geological Survey and Mines Bureau, Sri Lanka
Seismic crustal model of Sri Lanka
GFZ Data Services
2020
seismic crustal structure
Bayesian joint inversion
seismic ambient noise
receiver functions
BayHunter
Sri Lanka
EARTH SCIENCE SERVICES > MODELS
EARTH SCIENCE > SOLID EARTH
lithosphere > earth's crust
science > natural science > earth science > geophysics
eng
10.1029/2011JB008560
10.5880/GFZ.2.4.2019.001
10.1029/2019JB018688
10.14470/6N7561723039
CC BY 4.0
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.
79.5
82
5.5
9.5