Skeletonized wave-equation inversion for Q

Dutta, Gaurav

doi:10.1190/segam2016-13839306.1

Cited by 9 publications

(6 citation statements)

References 28 publications

(30 reference statements)

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“…They are similar to those for the acausal Q model in Figs 1(a) and (b), except now much of the energy arrives past the propagation time denoted by the blue vertical bar. If the Q distribution is known, then there are schemes for partial attenuation compensation in migration images (Wang & Guo 2004;Dutta & Schuster 2014, 2016Dutta 2016).…”

Section: Migration Stretch Limits For Causal Lossy Mediamentioning

confidence: 99%

Resolution limits of migration and linearized waveform inversion images in a lossy medium

Schuster¹,

Dutta²,

Li³

2017

Geophysical Journal International

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CitationSchuster GT, Dutta G, Li J (2017) Resolution limits of migration and linearized waveform inversion images in a lossy medium. S U M M A R YThe vertical-and horizontal-resolution limits x lossy and z lossy of post-stack migration and linearized waveform inversion images are derived for lossy data in the far-field approximation. Unlike the horizontal resolution limit x ∝ λz/L in a lossless medium which linearly worsens in depth z, x lossy ∝ z 2 /QL worsens quadratically with depth for a medium with small Q values. Here, Q is the quality factor, λ is the effective wavelength, L is the recording aperture, and loss in the resolution formulae is accounted for by replacing λ with z/Q. In contrast, the lossy vertical-resolution limit z lossy only worsens linearly in depth compared to z ∝ λ for a lossless medium. For both the causal and acausal Q models, the resolution limits are linearly proportional to 1/Q for small Q. These theoretical predictions are validated with migration images computed from lossy data.

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Section: Migration Stretch Limits For Causal Lossy Mediamentioning

confidence: 99%

Resolution limits of migration and linearized waveform inversion images in a lossy medium

Schuster¹,

Dutta²,

Li³

2017

Geophysical Journal International

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“…If no such event exists, the method is not applicable. Another set of methods Robust Q estimation using surface seismic data 3 make use of instantaneous frequency estimates in order to generate an objective function ( Chen et al, 2018, Dutta and Schuster, 2016, He and Cai, 2012, Hu et al, 2011.…”

Section: Introductionmentioning

confidence: 99%

Robust Q estimation using surface seismic data

Blanch¹

2020

Preprint

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Direct wave arrivals are the most robust signals to determine velocity and consequently they have been used for almost a century in hydrocarbon exploration. The reason is simple as the arrivaltime is explicitly available and provide a direct measurement of the average velocity of the sub-surface ray-path. These signals have not been extensively used to estimate attenuation or Q. One reason may be that very few robust methods have been developed to estimate Q from these signals. The common method to estimate Q using data acquired from VSP measurements is relying on spectral ratios. This method is in general quite reliable whenever there is a signal that has a reasonably broad spectral support. I will show how a variation on the spectral ratio method that provides equal results while more robust and how it can be adapted for surface seismic measurement.

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“…where, q and s are the adjoint-state variables of pressure P and memory variable r p , respectively (Blanch and Symes, 1994;Dutta andSchuster, 2014, 2016). Equation 17 can also be represented by the matrix-vector notation…”

Section: Deblurring Filters In a Viscoacoustic Mediummentioning

confidence: 99%

“…For these data, the migration velocity and Q models are shown in Figures 15a and 15b, respectively. The migration velocity model is estimated by early-arrival waveform inversion and the migration Q model is estimated by wave-equation Q tomography (Dutta, 2016;Dutta and Schuster, 2016).…”

Section: Friendswood Crosswell Field Datamentioning

confidence: 99%

Q-least-squares reverse time migration with viscoacoustic deblurring filters

et al. 2017

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Viscoacoustic least-squares reverse time migration, also denoted as Q-LSRTM, linearly inverts for the subsurface reflectivity model from lossy data. Compared to the conventional migration methods, it can compensate for the amplitude loss in the migrated images due to strong subsurface attenuation and can produce reflectors that are accurately positioned in depth. However, the adjoint Q propagators used for backward propagating the residual data are also attenuative. Thus, the inverted images from Q-LSRTM are often observed to have lower resolution when compared to the benchmark acoustic LSRTM images from acoustic data. To increase the resolution and accelerate the convergence of Q-LSRTM, we propose using viscoacoustic deblurring filters as a preconditioner for Q-LSRTM. These filters can be estimated by matching a simulated migration image to its reference reflectivity model.

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Skeletonized wave-equation inversion for Q

Cited by 9 publications

References 28 publications

Resolution limits of migration and linearized waveform inversion images in a lossy medium

Resolution limits of migration and linearized waveform inversion images in a lossy medium

Robust Q estimation using surface seismic data

Q-least-squares reverse time migration with viscoacoustic deblurring filters

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