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Fast and cheap approximation of Green function uncertainty for waveform-based earthquake source inversions

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10331062" target="_blank" >RIV/00216208:11320/16:10331062 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1093/gji/ggw320" target="_blank" >http://dx.doi.org/10.1093/gji/ggw320</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1093/gji/ggw320" target="_blank" >10.1093/gji/ggw320</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Fast and cheap approximation of Green function uncertainty for waveform-based earthquake source inversions

  • Original language description

    Green functions (GFs) are an essential ingredient in waveform-based earthquake source inversions. Hence, the error due to imprecise knowledge of a crustal velocity model is one of the major sources of uncertainty of the inferred earthquake source parameters. Recent strategies in Bayesian waveform inversions rely on statistical description of the GF uncertainty by means of a Gaussian distribution characterized by a covariance matrix. Here we use Monte-Carlo approach to estimate the GF covariance considering randomly perturbed velocity models. We analyse the dependence of the covariance on various parameters (strength of velocity model perturbations, GF frequency content, source-station distance, etc.). Recognizing that the major source of the GF uncertainty is related to the random time shifts of the signal, we propose a simplified approach to obtain approximate covariances, bypassing the numerically expensive Monte-Carlo simulations. The resulting closed-form formulae for the approximate auto-covariances and cross-covariances between stations and components can be easily implemented in existing inversion techniques. We demonstrate that the approximate covariances exhibit very good agreement with the Monte-Carlo estimates, providing realistic variations of the GF waveforms. Furthermore, we show examples of implementation of the covariance matrix in a Bayesian moment tensor inversion using both synthetic and real data sets. We demonstrate that taking the GF uncertainty into account leads to improved estimates of the moment tensor parameters and their uncertainty.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    DC - Seismology, volcanology and Earth structure

  • OECD FORD branch

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Geophysical Journal International

  • ISSN

    0956-540X

  • e-ISSN

  • Volume of the periodical

    207

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    18

  • Pages from-to

    1012-1029

  • UT code for WoS article

    000386453200024

  • EID of the result in the Scopus database

    2-s2.0-84994759312