Rahimi Dalkhani, Amin
Weemstra, Cornelis
Ágústsdóttir, Thorbjörg
Gudnason, Egill Árni
Hersir, Gylfi Páll
Zhang, Xin
Data underlying the publication: Transdimensional ambient-noise surface wave tomography of the Reykjanes Peninsula, SW Iceland
<p>This package includes a 3D shear wave velocity model of the Reykjanes Peninsula which is explained in detail in our accepted manuscript for publication in Geophysical Journal International (Rahimi Dalkhani et al., 2023). The ambient noise data were recorded in 2014-2015 in the context of the IMAGE (Integrated Methods for Advanced Geothermal Exploration) project. For details regarding the IMAGE project, we refer to Hersir et al. (2020) and Blanck et al. (2020). The open-access multi-institutional IMAGE data set is available at <a href="https://doi.org/10.14470/9Y7569325908" target="_blank">https://doi.org/10.14470/9Y7569325908</a>. For further processing steps applied to the data, phase velocity retrieval algorithm, tomography algorithm, and geological interpretation of the recovered shear wave velocity model, see Rahimi Dalkhani et al. (2023).</p><p><br></p><p>In this study, we used a recently developed probabilistic tomographic algorithm (Zhang et al., 2018; Rahimi Dalkhani et al., 2021) to perform Ambient noise surface wave tomography of the Reykjanes peninsula. The shear wave velocities obtained in this study result from a 3D, one-step Bayesian tomographic inversion (Zhang et al., 2018), which has its roots in the transdimensional inversion algorithm introduced by Bodin & Sambridge (2009). Rahimi Dalkhani et al. (2021) modified the algorithm in the sense that they update the ray paths less frequently (i.e., not at every perturbation step), while at the same time still honoring the non-linear aspect of the tomographic problem. They tested the modified algorithm on synthetic station-station travel times generated for the configuration of the extended IMAGE seismic network and the surface wave frequencies of interest (i.e., 0.1-0.5 Hz). In this study, we applied the modified algorithm to the extended IMAGE data set. First, we retrieved station-station surface wave phase travel times from the time-corrected ambient noise recordings (Weemstra et al., 2021). Then, we used these surface waves’ dispersion curves to generate 3D images of the RP subsurface’ shear wave velocity using the mentioned one-step transdimensional tomography algorithm. Finally, we interpret the recovered shear wave velocities, discuss how they compare to other recent geophysical studies, and list the most important conclusions (see Rahimi Dalkhani et al., 2023 for details).</p><p><br></p><p>In addition to the recovered shear wave velocity model of the Reykjanes peninsula, we included the following data and MATLAB scripts for the sake of the reproducibility of our results:</p><ol><li>Input files for the probabilistic inversion algorithm (i.e., MCTomo). The MCTomo software is open access and available at "https://blogs.ed.ac.uk/imaging/research/codes/" entitled "3D Monte Carlo tomography using both body and surface wave data". For more details see Rahimi Dalkhani et al. (2021, 2023). The modified package is available upon request.</li><li>The retrieved dispersion curves that we used in our inversion algorithm both as frequency-dependent travel times and phase velocities.</li><li>MATLAB data and scripts for reproducing all figures in our manuscript (Rahimi Dalkhani et al., 2023). It is worth mentioning that the MATLAB codes related to the phase velocity retrieval algorithm explained in Appendix A of Rahimi Dalkhani et al. (2023) are also included in the MATLAB scripts. See the script "FigureA1.m" for an example of how to use the codes.</li></ol><p>A "readme" file is included in each folder explaining the structure of the data and instructions. For the list of references we cited here see "ReferencesList.txt" in the main directory of the data.</p><p><br></p>
en
Reykjanes Peninsula
Shear wave velocity
Transdimensional Tomography
Crustal structure
Ambient noise tomography
2023
https://doi.org/10.4121/3c97b1c8-1736-495d-a2f9-bd26dc958575.v1

10.4121/3c97b1c8-1736-495d-a2f9-bd26dc958575.v1