Time-shift imaging condition in seismic migration

Sava, Paul; Fomel, Sergey

doi:10.1190/1.2338824

Cited by 320 publications

(174 citation statements)

References 21 publications

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“…The extended imaging condition (Rickett & Sava, 2002;Sava & Fomel, 2006;Sava & Vasconcelos, 2011) is similar to the conventional imaging condition except the cross-correlation lags between source and receiver wavefield are preserved in the output:…”

Section: Extended Imaging Conditionmentioning

confidence: 99%

“…Using extended images allows to measure the accuracy of the velocity model by analyzing the moveout of the events (Yang & Sava, 2010), and we can perform transformations from the extended to the angle domain (Sava & Fomel, 2006;Sava & Vlad, 2011;Sava & Fomel, 2003). The extended images provide a measurement of the similarity between the source and receiver wavefields along space and time, so we can exploit these images to analyze and better understand the RTM backscattered events.…”

Section: Extended Imaging Conditionmentioning

confidence: 99%

“…In this case the signal is defined as the reflectivity and the noise is the backscattered energy (Guitton et al, 2007). Finally, extended imaging conditions (Rickett & Sava, 2002;Sava & Fomel, 2006;Sava & Vasconcelos, 2011) provide information about the wavefield similarity for different space and/or time lags and can also be used to discriminate the backscattered energy. Kaelin & Carvajal (2011) In this article we analyze the information carried by the backscattered energy in the extended images.…”

Section: Introductionmentioning

confidence: 99%

“…One way to formulate the problem in the image space is by using extended images (Rickett & Sava, 2002;Sava & Fomel, 2006;Sava & Vasconcelos, 2011), from which we can extract wavefield similarities in space and time (Shen et al, 2003;Weibull & Arntsen, 2013;Yang & Sava, 2010). Extended images are normally used for optimizing the reflected data information, but Díaz & Sava (2012) show that similar to reflected data, the RTM backscattered energy also maps to zero time-lag and space-lag when the velocity model is correct.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the reverse time migration backscattering: noise or signal?

Díaz

Sava

2015

Geophysical Prospecting

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Reverse time migration (RTM) backscattered events are produced by the cross-correlation between waves reflected from sharp interfaces (e.g. the top of salt bodies). Commonly, these events are seen as a drawback for the RTM method because they obstruct the image of the geologic structure. Many strategies have been developed to filter out the artifacts from the conventional image. However, these events contain information that can be used to analyze kinematic synchronization between source and receiver wavefields reconstructed in the subsurface. Numeric and theoretical analysis indicate the sensitivity of the backscattered energy to velocity accuracy: an accurate velocity model maximizes the backscattered artifacts. The analysis of RTM extended images can be used as a quality control tool and as input to velocity analysis designed to constrain salt models and sediment velocity.The analysis in this thesis suggest that we can use backscattering events along with reflection data to define a joint optimization problem for velocity model building. The gradient required for model optimization suffers from cross-talk, similar to the more conventional RTM images. In order to avoid the cross-talk, I use a directional filter based on Poynting vectors which preserves the components of the wavefield traveling in the same direction.Using backscattered waves for constraining the velocity in the sediment section requires defining the top of salt in advance, which implies a dynamic workflow for model building in salt environments where both sediment velocity and salt interface change iteratively during inversion.

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Section: Extended Imaging Conditionmentioning

confidence: 99%

Section: Extended Imaging Conditionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding the reverse time migration backscattering: noise or signal?

Díaz

Sava

2015

Geophysical Prospecting

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“…It is motivated by the work of van Leeuwen and Mulder (2008b), who proposed offset-or time-shifts in the data domain as an alternative to the subsurface space-or time-shifts introduced for wave-equation migration velocity analysis (Faye and Jeannot, 1986;MacKay and Abma, 1992;Rickett and Sava, 2002;Sava and Fomel, 2006;Shen et al, 2003). The latter may fail in the presence of multiples (Mulder and van Leeuwen, 2008).…”

Section: Introductionmentioning

confidence: 99%

The Diagonalator, an Alternative Cost Functional for Wave-equation Inversion

Moghaddam¹,

Mulder²

2012

Proceedings

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SUMMARYThe classic least-squares cost functional for full waveform inversion suffers from local minima due to loop skipping in the absence of low frequencies in the seismic data. Velocity model building based on subsurface spatial or temporal shifts may break down in the presence of multiples in the data. Cost functionals that translate this idea to the data domain, with offset-or time-shifts, can handle multiples. An earlier data-domain formulation suffered from cross-talk between events. Here, we present a multishot extension that should be less sensitive to cross-talk. It has the property of an annihilator, similar to the functional used for velocity analysis with extended images based on subsurface shifs. However, since it operates in the data domain, it should be able to handle multiples. For 2-D models with line acquistion, the proposed functional applies a singular-value decomposition on the observed data and uses the eigenvectors to build data panel that should be diagonal in the correct velocity model, but has significant off-diagonal entries in the wrong model. By minimizing these offdiagonal entries or maximizing the main diagonal, the correct model should be found. We therefore named it the diagonalator. We present initial tests on a simple, horizontally layered velocity model.

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