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Seminar by Kathrin Smetana
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    :bdg-primary-line:`Speaker`

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        Kathrin Smetana (Stevens Institute of Technology)

    :bdg-primary-line:`Title`

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        Model order reduction for seismic applications

    :bdg-primary-line:`Date`

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        * March 26, 2024 15:00 CET+0100 (Europe/Rome)
        * March 26, 2024 10:00 EDT-0400 (US/Eastern)
        * March 26, 2024 09:00 CDT-0500 (US/Central)
        * March 26, 2024 07:00 PDT-0700 (US/Pacific)

    :bdg-primary-line:`Abstract`

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        Full waveform inversion to monitor changes in seismicity is a
        computationally expensive and challenging task. The latter is due to the
        fact that the discretization of the seismic wave equation can have
        millions of degrees of freedom. Moreover, aiming at estimating, for
        instance, the elastic structure at every grid point results in a large
        parameter space within the inverse problem. Model order reduction (MOR)
        techniques can help to speed up the computations, using low-dimensional
        models that capture the original system’s important features. However,
        for large-scale wave propagation problems, constructing efficient
        reduced models is challenging as MOR methods can suffer from a slow
        decay of the Kolmogorov n-width for such problems, thus, requiring a
        large number of basis functions to reach the desired accuracy. In our
        work, we address the mentioned challenge as follows: We transform the
        problem to the Laplace domain, where we can exploit that the output of
        interest – the seismogram – is band-limited or low-pass filtered, such
        that we can avoid high frequencies; the latter would require many
        reduced basis functions for an accurate approximation. By targeting the
        construction of the reduced model to the seismogram, we obtain a rapidly
        converging reduced order approximation.

    :bdg-primary-line:`Recording`

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        Watch the recording on `our YouTube channel <https://www.youtube.com/watch?v=C3nrLnHlFug>`_.