Imaging direct as well as scattered events in microseismic data using inverse scattering theory

Behura, Jyoti; Snieder, Roel

doi:10.1190/segam2013-1094.1

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…If a smooth model is used instead, the image could be substantially different from the true image. In the absence of a detailed and accurate velocity model, an alternate approach suggested by Behura & Snieder (2013) could be used. It is noteworthy that even if the true Green's function is used in equation 1, reverse-time imaging will yield a source signature that is scaled by xr |G| 2 ; therefore, the image obtained using reverse-time imaging will not be accurate unless the subsurface velocity and density are actually slowly varying such that |G| 2 = δ.…”

Section: Importance Of the Subsurface Modelmentioning

confidence: 99%

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Improving microseismic imaging: Role of acquisition, velocity model, and imaging condition

Behura

Forghani

Bazargani

2013

SEG Technical Program Expanded Abstracts 2013

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Section: Importance Of the Subsurface Modelmentioning

confidence: 99%

“…The images correspond to a depth of 5700 ft. (f) Depiction of the image slices displayed in (a)-(e). We use a smoothed version of the velocity model in Figure 6 and the imaging algorithm of Behura & Snieder (2013) for obtaining the exact Green's functions.…”

Section: Imaging Conditionsmentioning

confidence: 99%

Improving microseismic imaging: Role of acquisition, velocity model, and imaging condition

Behura

Forghani

Bazargani

2013

SEG Technical Program Expanded Abstracts 2013

Self Cite

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“…This expression replaces the intuitive microseismic imaging condition of Behura and Snieder (2013), which contains the time-reversed Marchenko-retrieved Green's function instead of the focusing function F(x, x R ,t).…”

Section: Induced Seismicity Representationsmentioning

confidence: 99%

Marchenko method for monitoring induced seismicity with virtual receivers

Wapenaar

Brackenhoff

Thorbecke

2018

SEG Technical Program Expanded Abstracts 2018

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The Marchenko method can be used to retrieve Green's functions (including multiple scattering) between virtual sources in the subsurface and physical receivers at the surface or virtual receivers in the subsurface. Here we discuss a variant of the Marchenko method which retrieves the response between physical sources and virtual receivers in the subsurface. We discuss the theory and illustrate it with numerical examples. The main application of the proposed method is monitoring of induced seismicity with virtual receivers in the subsurface.

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“…Moreover, combining Marchenko methods and convolutional interferometry allows estimating internal multiples in the data at the surface (Meles et al (2015);da Costa Filho et al (2017b)). Other applications of the Marchenko method include microseismic source localization (Behura et al (2013); ; Brackenhoff et al (2019)), inversion (van der Neut and Fokkema (2018)), homogeneous Green's functions retrieval (Reinicke and Wapenaar (2019); Wapenaar et al (2018)) and various wavefield focusing techniques (Meles et al (2019)). Despite its requirements on the quality of the reflection data, and more specifically its frequency content, the Marchenko scheme has already been successfully applied to a number of field datasets (Ravasi et al (2016); ; Jia et al (2018);da Costa Filho et al (2017a); Staring et al (2018)).…”

Section: Introductionmentioning

confidence: 99%

Data-driven synthesis of primary plane-wave responses

Meles¹,

Zhang²,

Thorbecke³

et al. 2020

Preprint

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Seismic images provided by Reverse Time Migration can be contaminated by artefacts associated with the migration of multiples. Multiples can corrupt seismic images producing both false positives, i.e. by focusing energy at unphysical interfaces, and false negatives, i.e. by destructively interfering with primaries. A pletora of algorithms have been developed to mitigate the impact of multiples in migration schemes, either through their prediction and subsequent adaptive subtraction, or by synthesis of primaries. Multiple prediction / primary synthesis methods are usually designed to operate on point source gathers, and can therefore be computationally demanding when large problems are considered. Here, a new scheme is presented for fully data-driven retrieval of primary responses to plane-wave sources. The proposed scheme, based on convolutions and cross-correlations of the reflection response with itself, extends a recently devised point-sources primary retrieval method for to plane-wave source data. As a result, the presented algorithm allows fully data-driven synthesis of primary reflections associated with plane-wave source data. Once primaries are estimated, they can be used for multiple-free imaging via a single migration step. The potential and limitations of the method are discussed on 2D acoustic synthetic examples.

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Imaging direct as well as scattered events in microseismic data using inverse scattering theory

Cited by 6 publications

References 11 publications

Improving microseismic imaging: Role of acquisition, velocity model, and imaging condition

Improving microseismic imaging: Role of acquisition, velocity model, and imaging condition

Marchenko method for monitoring induced seismicity with virtual receivers

Data-driven synthesis of primary plane-wave responses

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