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A Curtis
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A number of seismic processing methods, including velocity analysis (Sheriff and Geldart, 1999), make the assumption that recorded waves are primaries - that they have scattered only once (the Born approximation). Multiples then represent a source of coherent noise and must be suppressed to avoid artefacts. There are different approaches to mitigate free surface multiples (see Dragoset et al. (2010) for an overview), but internal multiples still pose a problem and usually cannot be removed without high computational cost or knowledge of the medium. Recently, Marchenko redatuming has been developed to image a medium in the presence of internal multiples (Wapenaar et al., 2014). Using Marchenko redatuming in combination with convolutional interferometry, Meles et al. (2016) have developed a method which allows the construction of a primaries-only data set from existing seismic reflection data and an initial velocity model. The method was proposed for the acoustic case and appears to be robust with respect to even huge inaccuracies in the employed velocity model. In this paper we investigate the impact of such primaries-only data on a simple velocity analysis workflow, as opposed to using the full data set with multiples. We use semblance analysis (Sheriff and Geldart, 1999) and compare the results obtained with three different data sets: the full reflection data with multiples, primaries data calculated with prior knowledge of the subsurface, and primaries data calculated with an entirely incorrect constant velocity model. We then use the velocity models that we construct to perform reverse time migration (RTM) of each of the data sets. We find that the velocities found are robust with respect to errors in the initial model used for Marchenko redatuming, and the method produces good results if non-hyperbolic moveout effects are avoided.
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A number of seismic processing methods, including velocity analysis (Sheriff and Geldart, 1999), make the assumption that recorded waves are primaries - that they have scattered only once (the Born approximation). Multiples then represent a source of coherent noise and must be suppressed to avoid artefacts. There are different approaches to mitigate free surface multiples (see Dragoset et al. (2010) for an overview), but internal multiples still pose a problem and usually cannot be removed without high computational cost or knowledge of the medium. Recently, Marchenko redatuming has been developed to image a medium in the presence of internal multiples (Wapenaar et al., 2014). Using Marchenko redatuming in combination with convolutional interferometry, Meles et al. (2016) have developed a method which allows the construction of a primaries-only data set from existing seismic reflection data and an initial velocity model. The method was proposed for the acoustic case and appears to be robust with respect to even huge inaccuracies in the employed velocity model. In this paper we investigate the impact of such primaries-only data on a simple velocity analysis workflow, as opposed to using the full data set with multiples. We use semblance analysis (Sheriff and Geldart, 1999) and compare the results obtained with three different data sets: the full reflection data with multiples, primaries data calculated with prior knowledge of the subsurface, and primaries data calculated with an entirely incorrect constant velocity model. We then use the velocity models that we construct to perform reverse time migration (RTM) of each of the data sets. We find that the velocities found are robust with respect to errors in the initial model used for Marchenko redatuming, and the method produces good results if non-hyperbolic moveout effects are avoided.
State-of-the-art methods to image the earth’s subsurface using active-source seismic reflection data involve reverse time migration. This and other standard seismic processing methods such as velocity analysis provide best results only when all waves in the data set are primaries (waves reflected only once). A variety of methods are therefore deployed as processing to predict and remove multiples (waves reflected several times); however, accurate removal of those predicted multiples from the recorded data using adaptive subtraction techniques proves challenging, even in cases in which they can be predicted with reasonable accuracy. We present a new, alternative strategy to construct a parallel data set consisting only of primaries, which is calculated directly from recorded data. This obviates the need for multiple prediction and removal methods. Primaries are constructed by using convolutional interferometry to combine the first-arriving events of upgoing and direct-wave downgoing Green’s functions to virtual receivers in the subsurface. The required upgoing wavefields to virtual receivers are constructed by Marchenko redatuming. Crucially, this is possible without detailed models of the earth’s subsurface reflectivity structure: Similar to the most migration techniques, the method only requires surface reflection data and estimates of direct (nonreflected) arrivals between the virtual subsurface sources and the acquisition surface. We evaluate the method on a stratified synclinal model. It is shown to be particularly robust against errors in the reference velocity model used and to improve the migrated images substantially.
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State-of-the-art methods to image the earth’s subsurface using active-source seismic reflection data involve reverse time migration. This and other standard seismic processing methods such as velocity analysis provide best results only when all waves in the data set are primaries (waves reflected only once). A variety of methods are therefore deployed as processing to predict and remove multiples (waves reflected several times); however, accurate removal of those predicted multiples from the recorded data using adaptive subtraction techniques proves challenging, even in cases in which they can be predicted with reasonable accuracy. We present a new, alternative strategy to construct a parallel data set consisting only of primaries, which is calculated directly from recorded data. This obviates the need for multiple prediction and removal methods. Primaries are constructed by using convolutional interferometry to combine the first-arriving events of upgoing and direct-wave downgoing Green’s functions to virtual receivers in the subsurface. The required upgoing wavefields to virtual receivers are constructed by Marchenko redatuming. Crucially, this is possible without detailed models of the earth’s subsurface reflectivity structure: Similar to the most migration techniques, the method only requires surface reflection data and estimates of direct (nonreflected) arrivals between the virtual subsurface sources and the acquisition surface. We evaluate the method on a stratified synclinal model. It is shown to be particularly robust against errors in the reference velocity model used and to improve the migrated images substantially.
Recently, a novel method to redatum the wavefield in the sub-surface from a reflection response measured at the surface has gained interest for imaging primaries in the presence of strong internal multiples. A prerequisite for the algorithm is an accurate and correct estimate of the direct-wave Green's function. However, usually we use an estimate for the direct-wave Green's function computed in a background velocity medium. Here, we investigate the effect of amplitude and phase errors in that estimate. We formulate two novel imaging conditions based on double-focusing the measured reflection response inside the subsurface. These yield information on the amplitude error in the estimate for the direct-wave Green's function which we can then correct, but the phase error remains elusive.
...
Recently, a novel method to redatum the wavefield in the sub-surface from a reflection response measured at the surface has gained interest for imaging primaries in the presence of strong internal multiples. A prerequisite for the algorithm is an accurate and correct estimate of the direct-wave Green's function. However, usually we use an estimate for the direct-wave Green's function computed in a background velocity medium. Here, we investigate the effect of amplitude and phase errors in that estimate. We formulate two novel imaging conditions based on double-focusing the measured reflection response inside the subsurface. These yield information on the amplitude error in the estimate for the direct-wave Green's function which we can then correct, but the phase error remains elusive.
Constructing Only the Primary Reflections in Seismic Data
Without Multiple Removal
State of the art methods to image the Earth's subsurface using active-source seismic reflection data involve reverse-time migration (RTM). This, and other standard seismic processing methods such as velocity analysis, provide best results only when all waves in the data set are primaries (waves reflected only once). A variety of methods are therefore deployed as pre-processing to predict multiples (waves reflected several times); however, accurate removal of those predicted multiples from recorded data using adaptive subtraction techniques proves challenging, even in cases where they can be predicted with reasonable accuracy. We describe a new, alternative strategy: we construct a parallel data set consisting of only primaries, which is calculated directly from recorded data. This obviates the need for both multiple prediction and removal methods. Primaries are constructed using convolutional interferometry to combine first arriving events of up-going and direct-wave down-going Green's functions to virtual receivers in the subsurface. The required up-going wavefields to virtual receivers are constructed by Marchenko redatuming. Crucially, this is possible without detailed models of the Earth's subsurface velocity structure. The method is shown both to be particularly robust against errors in the reference velocity model used, and to improve migrated images substantially.
...
State of the art methods to image the Earth's subsurface using active-source seismic reflection data involve reverse-time migration (RTM). This, and other standard seismic processing methods such as velocity analysis, provide best results only when all waves in the data set are primaries (waves reflected only once). A variety of methods are therefore deployed as pre-processing to predict multiples (waves reflected several times); however, accurate removal of those predicted multiples from recorded data using adaptive subtraction techniques proves challenging, even in cases where they can be predicted with reasonable accuracy. We describe a new, alternative strategy: we construct a parallel data set consisting of only primaries, which is calculated directly from recorded data. This obviates the need for both multiple prediction and removal methods. Primaries are constructed using convolutional interferometry to combine first arriving events of up-going and direct-wave down-going Green's functions to virtual receivers in the subsurface. The required up-going wavefields to virtual receivers are constructed by Marchenko redatuming. Crucially, this is possible without detailed models of the Earth's subsurface velocity structure. The method is shown both to be particularly robust against errors in the reference velocity model used, and to improve migrated images substantially.