Interferometric imaging of the underside of a subducting crust

Poliannikov, Oleg V.; Rondenay, S.; Chen, Ling

doi:10.1111/j.1365-246x.2012.05389.x

Cited by 10 publications

(8 citation statements)

References 32 publications

(46 reference statements)

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“…The resulting traveltime is that of the reflection response from below, indicated by the dashed ray. This method works well for primary reflections, as is shown with numerical examples by Poliannikov (2011) andPoliannikov et al (2012). However, equation 43 breaks down for multiple reflections.…”

Section: Comparison With Source-receiver Interferometrymentioning

confidence: 76%

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Marchenko imaging

et al. 2014

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Traditionally, the Marchenko equation forms a basis for 1D inverse scattering problems. A 3D extension of the Marchenko equation enables the retrieval of the Green’s response to a virtual source in the subsurface from reflection measurements at the earth’s surface. This constitutes an important step beyond seismic interferometry. Whereas seismic interferometry requires a receiver at the position of the virtual source, for the Marchenko scheme it suffices to have sources and receivers at the surface only. The underlying assumptions are that the medium is lossless and that an estimate of the direct arrivals of the Green’s function is available. The Green’s function retrieved with the 3D Marchenko scheme contains accurate internal multiples of the inhomogeneous subsurface. Using source-receiver reciprocity, the retrieved Green’s function can be interpreted as the response to sources at the surface, observed by a virtual receiver in the subsurface. By decomposing the 3D Marchenko equation, the response at the virtual receiver can be decomposed into a downgoing field and an upgoing field. By deconvolving the retrieved upgoing field with the downgoing field, a reflection response is obtained, with virtual sources and virtual receivers in the subsurface. This redatumed reflection response is free of spurious events related to internal multiples in the overburden. The redatumed reflection response forms the basis for obtaining an image of a target zone. An important feature is that spurious reflections in the target zone are suppressed, without the need to resolve first the reflection properties of the overburden.

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Section: Comparison With Source-receiver Interferometrymentioning

confidence: 76%

“…In particular, it represents the retrieval of the reflection response from below, as proposed by Poliannikov (2011) and Poliannikov et al (2012). In their approach, the Green's functions represent responses of real sources in the subsurface.…”

Section: Comparison With Source-receiver Interferometrymentioning

confidence: 99%

Marchenko imaging

et al. 2014

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“…This idea was proposed by Poliannikov (2011) and can be interpreted as a special application of source-receiver interferometry (Curtis and Halliday 2010). Poliannikov, Rodenay and Chen (2012) demonstrated how this methodology can be used to image a subducting crust from below. Bakulin et al (2007b) applied interferometric redatuming using receivers in horizontal boreholes for reservoir imaging and monitoring.…”

Section: N T E R F E R O M E T R I C R E D a T U M I N G B Y C R O mentioning

confidence: 99%

Point‐spread functions for interferometric imaging

Neut

Wapenaar

2015

Geophysical Prospecting

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A B S T R A C TInterferometric redatuming is a data-driven method to transform seismic responses with sources at one level and receivers at a deeper level into virtual reflection data with both sources and receivers at the deeper level. Although this method has traditionally been applied by cross-correlation, accurate redatuming through a heterogeneous overburden requires solving a multidimensional deconvolution problem. Input data can be obtained either by direct observation (for instance in a horizontal borehole), by modelling or by a novel iterative scheme that is currently being developed. The output of interferometric redatuming can be used for imaging below the redatuming level, resulting in a so-called interferometric image. Internal multiples from above the redatuming level are eliminated during this process. In the past, we introduced point-spread functions for interferometric redatuming by cross-correlation. These point-spread functions quantify distortions in the redatumed data, caused by internal multiple reflections in the overburden. In this paper, we define point-spread functions for interferometric imaging to quantify these distortions in the image domain. These point-spread functions are similar to conventional resolution functions for seismic migration but they contain additional information on the internal multiples in the overburden and they are partly data-driven. We show how these point-spread functions can be visualized to diagnose image defocusing and artefacts. Finally, we illustrate how point-spread functions can also be defined for interferometric imaging with passive noise sources in the subsurface or with simultaneous-source acquisition at the surface.

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“…The result is that a Green's function between a source and a receiver can be estimated from seismograms recorded on an array of other receivers (herein referred to as a backbone array) from a set of other sources. This has led to the development of new algorithms for imaging [ Halliday and Curtis , ; Vasconcelos et al , ; Poliannikov , ; Poliannikov et al , ; Vasconcelos and Rickett , ; Ravasi and Curtis , ; Vasconcelos , ; Ravasi et al , ], noise removal [ Duguid et al , ], methods to correct for errors in inter‐receiver and inter‐source interferometry [ King and Curtis , ; Meles and Curtis , ], and methods to analyze and synthesize scattered wavefields (e.g., Löer et al []; Meles and Curtis []; Löer et al []). All of these methods involve using SRI for some form of spatial redatuming of recorded data.…”

Section: Introductionmentioning

confidence: 99%

Constructing new seismograms from old earthquakes: Retrospective seismology at multiple length scales

et al. 2015

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If energy emitted by a seismic source such as an earthquake is recorded on a suitable backbone array of seismometers, source‐receiver interferometry (SRI) is a method that allows those recordings to be projected to the location of another target seismometer, providing an estimate of the seismogram that would have been recorded at that location. Since the other seismometer may not have been deployed at the time at which the source occurred, this renders possible the concept of “retrospective seismology” whereby the installation of a sensor at one period of time allows the construction of virtual seismograms as though that sensor had been active before or after its period of installation. Here we construct such virtual seismograms on target sensors in both industrial seismic and earthquake seismology settings, using both active seismic sources and ambient seismic noise to construct SRI propagators, and on length scales ranging over 5 orders of magnitude from ∼40 m to ∼2500 km. In each case we compare seismograms constructed at target sensors by SRI to those actually recorded on the same sensors. We show that spatial integrations required by interferometric theory can be calculated over irregular receiver arrays by embedding these arrays within 2‐D spatial Voronoi cells, thus improving spatial interpolation and interferometric results. The results of SRI are significantly improved by restricting the backbone receiver array to include approximately those receivers that provide a stationary‐phase contribution to the interferometric integrals. Finally, we apply both correlation‐correlation and correlation‐convolution SRI and show that the latter constructs fewer nonphysical arrivals.

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Interferometric imaging of the underside of a subducting crust

Cited by 10 publications

References 32 publications

Marchenko imaging

Marchenko imaging

Point‐spread functions for interferometric imaging

Constructing new seismograms from old earthquakes: Retrospective seismology at multiple length scales

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