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Title: Balancing unevenly distributed data in seismic tomography: a global adjoint tomography example

Abstract

SUMMARY 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.

Authors:
ORCiD logo [1];  [2];  [3];  [4];  [5]; ORCiD logo [6]
  1. School of Earth Sciences and Engineering, Nanjing University, Nanjing, Jiangsu 210023, China, Department of Geosciences, Princeton University, Princeton, NJ 08520, USA
  2. Department of Geosciences, Princeton University, Princeton, NJ 08520, USA
  3. Department of Geosciences, Princeton University, Princeton, NJ 08520, USA, Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
  4. Laboratoire Géoazur, Université Côte d’Azur, Valbonne 06560, France
  5. Department of Geophysics, Colorado School of Mines, Golden, CO 80401, USA
  6. Department of Geosciences, Princeton University, Princeton, NJ 08520, USA, Program in Applied & Computational Mathematics, Princeton University, Princeton, NJ 08520, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1562774
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Published Article
Journal Name:
Geophysical Journal International
Additional Journal Information:
Journal Name: Geophysical Journal International Journal Volume: 219 Journal Issue: 2; Journal ID: ISSN 0956-540X
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Ruan, Youyi, Lei, Wenjie, Modrak, Ryan, Örsvuran, Rıdvan, Bozdağ, Ebru, and Tromp, Jeroen. Balancing unevenly distributed data in seismic tomography: a global adjoint tomography example. United Kingdom: N. p., 2019. Web. doi:10.1093/gji/ggz356.
Ruan, Youyi, Lei, Wenjie, Modrak, Ryan, Örsvuran, Rıdvan, Bozdağ, Ebru, & Tromp, Jeroen. Balancing unevenly distributed data in seismic tomography: a global adjoint tomography example. United Kingdom. doi:10.1093/gji/ggz356.
Ruan, Youyi, Lei, Wenjie, Modrak, Ryan, Örsvuran, Rıdvan, Bozdağ, Ebru, and Tromp, Jeroen. Thu . "Balancing unevenly distributed data in seismic tomography: a global adjoint tomography example". United Kingdom. doi:10.1093/gji/ggz356.
@article{osti_1562774,
title = {Balancing unevenly distributed data in seismic tomography: a global adjoint tomography example},
author = {Ruan, Youyi and Lei, Wenjie and Modrak, Ryan and Örsvuran, Rıdvan and Bozdağ, Ebru and Tromp, Jeroen},
abstractNote = {SUMMARY 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.},
doi = {10.1093/gji/ggz356},
journal = {Geophysical Journal International},
number = 2,
volume = 219,
place = {United Kingdom},
year = {2019},
month = {8}
}

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