Linearized versus non-linear inverse methods for seismic localization of underground sources

Geok Lian Oh, Finn Jacobsen

    Research output: Contribution to journalConference articleResearchpeer-review

    Abstract

    The problem of localization of underground sources from seismic measurements detected by several geophones located on the ground surface is addressed. Two main approaches to the solution of the problem are considered: a beamforming approach that is derived from the linearized inversion problem, and the Bayes nonlinear inversion method. The travel times used in the beamformer are derived from solving the Eikonal equation. In the linearized inversion method, we assume that the elastic waves are predominantly acoustic waves, and the acoustic approximation is applied. For the nonlinear inverse method, we apply the Bayesian framework where the misfit function is the posterior probability distribution of the model space. The model parameters are the location of the seismic source that we are interested in estimating. The forward problem solver applied for the nonlinear inverse method is a Finite Difference elastic wave-field numerical method. In this paper, the accuracy and performance of the linear beamformer and nonlinear inverse methods to localize a underground seismic source are checked and compared using computer generated synthetic experimental data. © 2013 Acoustical Society of America.
    Original languageEnglish
    JournalMeetings on Acoustics. Proceedings
    Volume19
    Number of pages6
    ISSN1939-800X
    DOIs
    Publication statusPublished - 2013
    Event21st International Congress on Acoustics - Montreal, Canada
    Duration: 2 Jun 20137 Jun 2013
    Conference number: 21
    http://www.ica2013montreal.org/

    Conference

    Conference21st International Congress on Acoustics
    Number21
    Country/TerritoryCanada
    CityMontreal
    Period02/06/201307/06/2013
    Internet address

    Keywords

    • Acoustics
    • Beamforming
    • Elastic waves
    • Geometrical optics
    • Linearization
    • Probability distributions
    • Seismology
    • Inverse problems

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