A local adaptive method for the numerical approximation in seismic wave modelling

Galuzzi, Bruno G.; Zampieri, E.; Stucchi, E.

doi:10.1515/caim-2017-0014

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…We considered 16 seismograms, recorded by a spread of 192 receivers, equally spaced 24 m. Both the sources and the receivers are at a depth of 24 m, and the recording time is T = 4 secs. The synthetic seismograms are obtained by solving the acoustic wave equation (9), using an efficient FD scheme, whose details of the implementation can be found in [8].…”

Section: The Marmousi Benchmarkmentioning

confidence: 99%

Bayesian Optimization for Full Waveform Inversion

Galuzzi

Perego

Candelieri

et al. 2018

AIRO Springer Series

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Full Waveform Inversion (FWI) is a computational method to estimate the physical features of Earth subsurface from seismic data, leading to the minimization of a misfit function between the observed data and the predicted ones, computed by solving the wave equation numerically. This function is usually multimodal, and any gradient-based method would likely get trapped in a local minimum, without a suitable starting point in the basin of attraction of the global minimum. The starting point of the gradient procedure can be provided by an exploratory stage performed by an algorithm incorporating random elements. In this paper, we show that Bayesian Optimization (BO) can offer an effective way to structure this exploration phase. The computational results on a 2D acoustic FWI benchmark problem show that BO can provide a starting point in the parameter space from which the gradient-based method converges to the global optimum.

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Section: The Marmousi Benchmarkmentioning

confidence: 99%

Bayesian Optimization for Full Waveform Inversion

Galuzzi

Perego

Candelieri

et al. 2018

AIRO Springer Series

Self Cite

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“…The synthetic data are computed using an explicit finite difference algorithm used to solve the 2D acoustic wave equation (Galuzzi et al 2017) where the order of approximation of the spatial derivatives is optimized to reduce the numerical dispersion. The model dimensions are approximately 7 km in length, and 2.4 km in depth and the modelling grid is made by 242x80 nodes, with a uniform grid size of dx=30m.…”

Section: Modellingmentioning

confidence: 99%

A Global-Local Experience of 2D Acoustic FWI on a Real Data Set

2018

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Acoustic global–local full‐waveform inversion for P‐wave velocity estimation of near‐surface seismic data acquired in Luni, Italy

Stucchi

Pierini

Tognarelli

et al. 2020

Near Surface Geophysics

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In this work, we describe an experiment concerning global–local full‐waveform inversion, carried out on a P‐wave seismic reflection profile that was acquired at Luni, an archaeological site in Italy. The global full‐waveform inversion makes use of a two‐grid genetic algorithm scheme and recorded refraction and diving waves, to build an initial velocity model of the subsurface. Two important pieces of a priori information which help to better constrain the inversion results are the refraction velocity model and the Dix‐converted semblance velocity field obtained from time processing. A good match between observed and predicted data allows us to use the estimated velocity field as the starting point for a local, gradient‐based full‐waveform inversion that inverts the recorded data (except the surface waves). The final estimated velocity field shows two main discontinuities: one is very shallow and related to the refractor velocity model used and the other corresponds to the strongest reflection event observed in the pre‐stack depth‐migrated section, at a depth of 100 m. The pre‐stack depth‐migrated common image gathers provide evidence of a good horizontal alignment of this reflection, indicating an accurate velocity estimation down to 100 m depth that corresponds to the maximum offset used in the acquisition.

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A local adaptive method for the numerical approximation in seismic wave modelling

Cited by 4 publications

References 23 publications

Bayesian Optimization for Full Waveform Inversion

Bayesian Optimization for Full Waveform Inversion

A Global-Local Experience of 2D Acoustic FWI on a Real Data Set

Acoustic global–local full‐waveform inversion for P‐wave velocity estimation of near‐surface seismic data acquired in Luni, Italy

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