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|Abstract:||The uneven distribution of earthquakes and stations in seismic tomography leads to slower convergence of nonlinear inversions and spatial bias in inversion results. Including dense regional arrays, such as USArray or Hi-Net, in global tomography causes severe convergence and spatial bias problems, against which conventional pre-conditioning schemes are ineffective. To save computational cost and reduce model bias, we propose a new strategy based on a geographical weighting of sources and receivers. Unlike approaches based on ray density or the Voronoi tessellation, this method scales to large full-waveform inversion problems and avoids instabilities at the edges of dense receiver or source clusters. We validate our strategy using a 2-D global waveform inversion test and show that the new weighting scheme leads to a nearly twofold reduction in model error and much faster convergence relative to a conventionally pre-conditioned inversion. We implement this geographical weighting strategy for global adjoint tomography.|
|Citation:||Ruan, Youyi, Wenjie Lei, Ryan Modrak, Rıdvan Örsvuran, Ebru Bozdağ, and Jeroen Tromp. "Balancing unevenly distributed data in seismic tomography: a global adjoint tomography example." Geophysical Journal International 219, no. 2 (2019): 1225-1236. doi:10.1093/gji/ggz356.|
|Pages:||1225 - 1236|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||Geophysical Journal International|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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