Towards a 3D Transdimensional Ambient-Noise Surface Wave Tomography of Reykjanes Peninsula, SW Iceland

Summary Ambient-noise surface wave tomography has proven to be an effective tool for 3D crustal imaging. Conventionally, a two-step inversion approach is adopted. That is, a first inversion results in separate, frequency-dependent 2D surface-wave velocity maps, upon which local frequency-to-depth inversions are performed. In our case, phase velocities are used. A one-step 3D non-linear algorithm has recently been proposed in a Bayesian framework using reversible jump Markov chain Monte Carlo which is called transdimensional tomography. This approach has a number of advantages over the two-step approach with the most notable being the fact that the one-step approach preserves spatial correlation information in 3D. The 3D volume is parameterized using a polyhedral Voronoi tessellation. Here, we investigate the feasibility of using this transdimensional algorithm to recover the 3D seismic velocity structure below the Reykjanes peninsula, SW Iceland. To that end, we design synthetic tests using the station configuration of Reykjanes seismic network, which has been recording the field data. We show that the algorithm successfully recovers the 3D velocity structure of the area. This work is a first step towards 3D non-linearized surface-wave tomography using the field data recorded by the stations of the Reykjanes seismic network.