Dataset for "CANVAS: An adjoint waveform tomography model of California and Nevada"

Doody, Claire

doi:10.5281/zenodo.8415562

Title: Dataset for "CANVAS: An adjoint waveform tomography model of California and Nevada"

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Authors:

Doody, Claire

Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Publication Date:: Sat Oct 07 00:00:00 EDT 2023

Other Number(s):: LLNL-MI-856789

DOE Contract Number:: AC52-07NA27344

Research Org.:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 2425863

DOI:: https://doi.org/10.5281/zenodo.8415562

Citation Formats


                    Doody, Claire. Dataset for "CANVAS: An adjoint waveform tomography model of California and Nevada".  United States: N. p., 2023. 
        Web.  doi:10.5281/zenodo.8415562.

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                    Doody, Claire. Dataset for "CANVAS: An adjoint waveform tomography model of California and Nevada".  United States.  doi:https://doi.org/10.5281/zenodo.8415562

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                    Doody, Claire. 2023.  
"Dataset for "CANVAS: An adjoint waveform tomography model of California and Nevada"".  United States.  doi:https://doi.org/10.5281/zenodo.8415562.  https://www.osti.gov/servlets/purl/2425863. Pub date:Sat Oct 07 00:00:00 EDT 2023

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@article{osti_2425863,

  title        = {Dataset for "CANVAS: An adjoint waveform tomography model of California and Nevada"},

  author       = {Doody, Claire},

  abstractNote = {},

  doi          = {10.5281/zenodo.8415562},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sat Oct 07 00:00:00 EDT 2023},

  month        = {Sat Oct 07 00:00:00 EDT 2023}

}

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DOI: https://doi.org/10.5281/zenodo.8415562

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CANVAS: An Adjoint Waveform Tomography Model of California and Nevada

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Abstract We present the California‐Nevada Adjoint Simulations (CANVAS) model, an adjoint waveform tomography model of the crust and uppermost mantle of the states of California and Nevada. We used WUS256 (Rodgers et al., 2022, https://doi.org/10.1029/2022jb024549 ) as the starting model and iteratively decreased the minimum period of CANVAS from 30 to 12 s. CANVAS was iterated in two distinct stages: the first stage with source mechanisms from the Global Centroid Moment Tensor (GCMT) catalog and the second stage with inverted moment tensors (MT) using the CANV_WUS model (Doody et al., 2023, https://doi.org/10.1029/2023jb026463 ). We show that updating the MTs with 3D Green's functionsmore »« less
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