Strike shooting, dip shooting, widepatch shooting — Does prestack depth migration care? A model study

Etgen, John; Regone, Carl

doi:10.1190/1.1820541

Cited by 40 publications

(19 citation statements)

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“…We consider a model based on a modified version of the Amoco data set, originally introduced by Etgen and Regone (1998), as shown in Figure 4a. Because our method allows for target-oriented imaging, we focus on the portion of the model enclosed by the solid rectangle in Figure 4a.…”

Section: Imagingmentioning

confidence: 99%

Data-driven wavefield focusing and imaging with multidimensional deconvolution: Numerical examples for reflection data with internal multiples

2014

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Standard imaging techniques rely on the single scattering assumption. This requires that the recorded data do not include internal multiples, i.e., waves that have bounced multiple times between reflectors before reaching the receivers at the acquisition surface. When multiple reflections are present in the data, standard imaging algorithms incorrectly image them as ghost reflectors. These artifacts can mislead interpreters in locating potential hydrocarbon reservoirs. Recently, we introduced a new approach for retrieving the Green's function recorded at the acquisition surface due to a virtual source located at depth. We refer to this approach as data-driven wavefield focusing. Additionally, after applying source-receiver reciprocity, this approach allowed us to decompose the Green's function at a virtual receiver at depth in its downgoing and upgoing components. These wavefields were then used to create a ghost-free image of the medium with either crosscorrelation or multidimensional deconvolution, presenting an advantage over standard prestack migration. We tested the robustness of our approach when an erroneous background velocity model is used to estimate the first-arriving waves, which are a required input for the datadriven wavefield focusing process. We tested the new method with a numerical example based on a modification of the Amoco model.

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Section: Imagingmentioning

confidence: 99%

Data-driven wavefield focusing and imaging with multidimensional deconvolution: Numerical examples for reflection data with internal multiples

2014

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“…O'Connell et al (1993) found that for many CMPbased processes, strike direction datasets had advantages over dip directions. Etgen and Regone (1998) showed that there are differences in multiple attenuation and illumination between strike and dip direction surveys. For these reasons, we can expect that the images produced by inverting two or more datasets shot over the same subsurface volume will contain different illumination information.…”

Section: Joint Inversionmentioning

confidence: 99%

Regularized least‐squares inversion for 3‐D subsalt imaging

Clapp

Biondi

2005

SEG Technical Program Expanded Abstracts 2005

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SUMMARYObtaining seismic images of the subsurface near and beneath salt is very difficult due to seismic energy that is lost by propagating outside of the survey area or becoming evanescent at salt boundaries (poor illumination). We demonstrate an iterative regularized least-squares inversion for imaging that helps to compensate for illumination problems. We show the use of a regularization operator that acts to regularize amplitudes along reflection angles (or equivalent offset ray parameters) to compensate for the sudden, large amplitude changes caused by poor illumination. This regularization operator has the effect of filling in the gaps created in the reflection angle range due to the lost seismic energy. We discuss the use of this regularization operator in an iterative least-squares inversion scheme to improve imaging for a poorly illuminated 3-D seismic dataset. We introduce a new type of joint inversion problem that will allows us to simultaneously invert two or more datasets. For this iterative inversion scheme, we design a different regularization operator that allows us to share illumination information between images created from the different datasets involved. This regularized inversion allows us to create images that share the illumination information from the different datasets.

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“…We consider a model based on a modified version of the Amoco dataset, originally introduced by Etgen and Regone (1998) We computed 501 shots with sources located at z = 0 Km. The acquisition surface is transparent; hence no surface-related multiples are present.…”

Section: Configurationmentioning

confidence: 99%

Data-driven Green's function retrieval and imaging with multidimensional deconvolution: Numerical examples for reflection data with internal multiples

Broggini

Snieder

Wapenaar

2013

SEG Technical Program Expanded Abstracts 2013

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SUMMARYStandard imaging techniques rely on the single scattering assumption. This requires that the recorded data do not include internal multiples, i.e. waves bouncing multiple times between layers before reaching the receivers at the acquisition surface. When multiple reflections are present in the data, standard imaging algorithms incorrectly image them as ghost reflectors. These artifacts can mislead the interpreters in locating potential hydrocarbon reservoirs. Recently, we introduced a new approach for retrieving the Greens function recorded at the acquisition surface due to a virtual source located at depth. Additionally, our approach allows us to decompose the Green's function in its downgoing and upgoing components. These wave fields are then used to create a ghostfree image of the medium with either crosscorrelation or multidimensional deconvolution, presenting an advantage over standard prestack migration. We illustrate the new method with a numerical example based on a modification of the Amoco model.

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Strike shooting, dip shooting, widepatch shooting — Does prestack depth migration care? A model study

Cited by 40 publications

References 0 publications

Data-driven wavefield focusing and imaging with multidimensional deconvolution: Numerical examples for reflection data with internal multiples

Data-driven wavefield focusing and imaging with multidimensional deconvolution: Numerical examples for reflection data with internal multiples

Regularized least‐squares inversion for 3‐D subsalt imaging

Data-driven Green's function retrieval and imaging with multidimensional deconvolution: Numerical examples for reflection data with internal multiples

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