Imaging of near-surface heterogeneities by scattered elastic waves

Almuhaidib, Abdulaziz M.; Toksöz, M. Nafi

doi:10.1190/geo2014-0416.1

Cited by 17 publications

(5 citation statements)

References 22 publications

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“…The scattered surface wave generated by strong heterogeneities in the shallow subsurface is often seen as noise in seismic reflection records (Blonk et al, 1995;Ernst et al, 2002); however, this noise can also be used as signal if the backscattered data are migrated to image the near-surface heterogeneities (Snieder, 1986;Riyanti, 2005;Yu et al, 2014;Almuhaidib and Toksöz, 2015;Hyslop and Stewart, 2015).…”

Section: Introductionmentioning

confidence: 99%

Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test

et al. 2017

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We have developed a methodology for detecting the presence of near-surface heterogeneities by naturally migrating backscattered surface waves in controlled-source data. The near-surface heterogeneities must be located within a depth of approximately one-third the dominant wavelength [Formula: see text] of the strong surface-wave arrivals. This natural migration method does not require knowledge of the near-surface phase-velocity distribution because it uses the recorded data to approximate the Green’s functions for migration. Prior to migration, the backscattered data are separated from the original records, and the band-passed filtered data are migrated to give an estimate of the migration image at a depth of approximately one-third [Formula: see text]. Each band-passed data set gives a migration image at a different depth. Results with synthetic data and field data recorded over known faults validate the effectiveness of this method. Migrating the surface waves in recorded 2D and 3D data sets accurately reveals the locations of known faults. The limitation of this method is that it requires a dense array of receivers with a geophone interval less than approximately one-half [Formula: see text].

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Section: Introductionmentioning

confidence: 99%

Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test

et al. 2017

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“…Through scattered waves, one can investigate shallower or deeper parts of the subsurface depending on the scale of the study, whether it is local or global. For engineering‐scale studies, investigation of the near surface is often crucial for detecting and monitoring structures that cause scattering of body and/or surface waves (Campman and Riyanti, 2007; Xia et al ., 2007; Kaslilar, 2007; Almuhaidib and Toksöz, 2015; Tremblay et al ., 2017; 2018; Borisov et al ., 2018; Harmankaya et al ., 2018; Anjom et al ., 2019). Surface waves are considered to be useful for near‐surface investigations due to their relatively high signal‐to‐noise ratio that dominates the shallow seismic wavefield (Pan et al ., 2019; Gao et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

Modelling 3D elastodynamic wave scattering due to density and Lamé parameter contrasts of near‐surface scatterers

Harmankaya

Kaslilar

2022

Near Surface Geophysics

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Investigating the near-surface structure of the Earth through geophysical methods is a crucial aspect in many areas of study in geotechnical engineering, environmental science and exploration. Among several geophysical methods, seismic-based ones are widely used for characterizing near-surface features that are distinguished by contrasts in elastic parameters. In this paper, we model 3D elastodynamic wave propagation and scattering using a method based on domain-type integral representation with Born approximation. We calculate the scattered wavefield by considering the first-order perturbations in density and Lamé parameter contrasts of scatterers. Contrasts in Lamé parameters can be useful for determining the material properties of subsurface structures in cases of weak contrasts in velocities accompanying considerable Lamé parameter variations. We examine the effects of each parameter contrast on a series of models involving a subsurface scatterer. We also compare the seismograms obtained from our method with those from a 3D finite-difference wavefield modelling program, where we observe good agreement between the modelling results. Sensitivity of the wavefield to the perturbation in each model parameter is also examined by calculating and analysing the Fréchet derivatives. In general, the method discussed here can provide a solid foundation for prospective imaging studies involving density and Lamé parameters simultaneously.

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“…To address these problems, some possible methods are based on single-scattering formulations (e.g. Rose 1989), or are based on migration using time reversal and an imaging condition (Almuhaidib & Toksöz 2015;Kocur et al 2016). In particular, the present study deals with a cavity detection problem.…”

Section: Introductionmentioning

confidence: 99%

Multiscale seismic imaging with inverse homogenization

Hedjazian

Capdeville

Bodin

2021

Geophysical Journal International

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Summary Seismic imaging techniques such as elastic full waveform inversion (FWI) have their spatial resolution limited by the maximum frequency present in the observed waveforms. Scales smaller than a fraction of the minimum wavelength cannot be resolved, and only a smoothed, effective version of the true underlying medium can be recovered. These finite-frequency effects are revealed by the upscaling or homogenization theory of wave propagation. Homogenization aims at computing larger scale effective properties of a medium containing small-scale heterogeneities. We study how this theory can be used in the context of FWI. The seismic imaging problem is broken down in a two-stage multiscale approach. In the first step, called homogenized full waveform inversion (HFWI), observed waveforms are inverted for a smooth, fully anisotropic effective medium, that does not contain scales smaller than the shortest wavelength present in the wavefield. The solution being an effective medium, it is difficult to directly interpret it. It requires a second step, called downscaling or inverse homogenization, where the smooth image is used as data, and the goal is to recover small-scale parameters. All the information contained in the observed waveforms is extracted in the HFWI step. The solution of the downscaling step is highly non-unique as many small-scale models may share the same long wavelength effective properties. We therefore rely on the introduction of external a priori information, and cast the problem in a Bayesian formulation. The ensemble of potential fine-scale models sharing the same long wavelength effective properties is explored with a Markov chain Monte Carlo algorithm. We illustrate the method with a synthetic cavity detection problem: we search for the position, size and shape of void inclusions in a homogeneous elastic medium, where the size of cavities is smaller than the resolving length of the seismic data. We illustrate the advantages of introducing the homogenization theory at both stages. In HFWI, homogenization acts as a natural regularization helping convergence toward meaningful solution models. Working with fully anisotropic effective media prevents the leakage of anisotropy induced by the fine scales into isotropic macro-parameters estimates. In the downscaling step, the forward theory is the homogenization itself. It is computationally cheap, allowing us to consider geological models with more complexity (e.g. including discontinuities) and use stochastic inversion techniques.

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Imaging of near-surface heterogeneities by scattered elastic waves

Cited by 17 publications

References 22 publications

Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test

Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test

Modelling 3D elastodynamic wave scattering due to density and Lamé parameter contrasts of near‐surface scatterers

Multiscale seismic imaging with inverse homogenization

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