Improved surface-wave retrieval from ambient seismic noise by multi-dimensional deconvolution

Wapenaar, Kees; Ruigrok, Elmer; Neut, Joost van der; Draganov, Deyan

doi:10.1029/2010gl045523

Cited by 42 publications

(32 citation statements)

References 22 publications

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“…Furthermore, they found that SI by MDD slightly corrects the phase of the retrieved responses at short offsets, resulting in more accurate phase-velocity estimates. The improvements found corroborate earlier numerical experiments considering a subset of USAarray stations illuminated by fundamental-mode surface wave noise excited along the eastern coast of the USA (Wapenaar et al 2011a).…”

Section: Introductionsupporting

confidence: 78%

“…The array has an aperture of approximately 60 km and has partly been designed for the application of SI by MDD (Ruigrok et al 2012). The denser receiver spacing of this array with respect to the deployments considered in Wapenaar et al (2011a) and van Dalen et al (2015), allows application of SI by MDD over a larger frequency range. The T-shape of the array lends itself well for the application of SI by MDD: it allows the construction of a PSF along one of the two receiver lines.…”

Section: Introductionmentioning

confidence: 99%

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Application of seismic interferometry by multidimensional deconvolution to ambient seismic noise recorded in Malargüe, Argentina

Weemstra

Draganov²,

Ruigrok

et al. 2016

Geophys. J. Int.

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S U M M A R YObtaining new seismic responses from existing recordings is generally referred to as seismic interferometry (SI). Conventionally, the SI responses are retrieved by simple crosscorrelation of recordings made by separate receivers: one of the receivers acts as a 'virtual source' whose response is retrieved at the other receivers. When SI is applied to recordings of ambient seismic noise, mostly surface waves are retrieved. The newly retrieved surface wave responses can be used to extract receiver-receiver phase velocities. These phase velocities often serve as input parameters for tomographic inverse problems. Another application of SI exploits the temporal stability of the multiply scattered arrivals of the newly retrieved surface wave responses. Temporal variations in the stability and/or arrival time of these multiply scattered arrivals can often be linked to temporally varying parameters such as hydrocarbon production and precipitation. For all applications, however, the accuracy of the retrieved responses is paramount. Correct response retrieval relies on a uniform illumination of the receivers: irregularities in the illumination pattern degrade the accuracy of the newly retrieved responses. In practice, the illumination pattern is often far from uniform. In that case, simple crosscorrelation of separate receiver recordings only yields an estimate of the actual, correct virtual-source response. Reformulating the theory underlying SI by crosscorrelation as a multidimensional deconvolution (MDD) process, allows this estimate to be improved. SI by MDD corrects for the non-uniform illumination pattern by means of a so-called point-spread function (PSF), which captures the irregularities in the illumination pattern. Deconvolution by this PSF removes the imprint of the irregularities on the responses obtained through simple crosscorrelation. We apply SI by MDD to surface wave data recorded by the Malargüe seismic array in western Argentina. The aperture of the array is approximately 60 km and it is located on a plateau just east of the Andean mountain range. The array has a T-shape: the receivers along one of the two lines act as virtual sources whose responses are recorded by the receivers along the other (perpendicular) line. We select time windows dominated by surface wave noise travelling in a favourable direction, that is, traversing the line of virtual sources before arriving at the receivers at which we aim to retrieve the virtual-source responses. These time windows are selected through a frequency-dependent slowness analysis along the two receiver lines. From the selected time windows, estimates of virtual-source responses are retrieved by means of crosscorrelations. Similarly, crosscorrelations between the positions of the virtual sources are computed to build C The

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Section: Introductionsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Application of seismic interferometry by multidimensional deconvolution to ambient seismic noise recorded in Malargüe, Argentina

Weemstra

Draganov²,

Ruigrok

et al. 2016

Geophys. J. Int.

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“…Contrary to the CC method, the MDD method is valid for a dissipative medium and compensates for differences in source spectra, and it is also valid for irregular source distributions. Due to these advantages, the MDD method has shown to be superior in some cases, such as in the retrieval of surface waves (Wapenaar et al, 2011a), in electromagnetic surveys (Wapenaar et al, 2008), in use with VSs (van der Neut et al, 2011), and in crosswell seismic data (Minato et al, 2011). On the other hand, MDD has several disadvantages compared to the CC method; MDD requires receiver arrays and cannot be applied to a single receiver configuration.…”

Section: Introductionmentioning

confidence: 99%

Singular-value decomposition analysis of source illumination in seismic interferometry by multidimensional deconvolution

2013

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We have developed a method to analytically evaluate the relationship between the source-receiver configuration and the retrieved wavefield in seismic interferometry performed by multidimensional deconvolution (MDD). The MDD method retrieves the wavefield with the desired source-receiver configuration from the observed wavefield without source information. We used a singular-value decomposition (SVD) approach to solve the inverse problem of MDD. By introducing SVD into MDD, we obtained quantities that revealed the characteristics of the MDD inverse problem and interpreted the effect of the initial sourcereceiver configuration for a survey design. We numerically simulated the wavefield with a 2D model and investigated the rank of the incident field matrix of the MDD inverse problem. With a source array of identical length, a sparse and a dense source distribution resulted in an incident field matrix of the same rank and retrieved the same wavefield. Therefore, the optimum source distribution can be determined by analyzing the rank of the incident field matrix of the inverse problem. In addition, the introduction of scatterers into the model improved the source illumination and effectively increased the rank, enabling MDD to retrieve a better wavefield. We found that the ambiguity of the wavefield inferred from the model resolution matrix was a good measure of the amount of illumination of each receiver by the sources. We used the field data recorded at the two boreholes from the surface sources to support our results of the numerical modeling. We evaluated the rank of incident field matrix with the dense and sparse source distribution. We discovered that these two distributions resulted in an incident field matrix of almost the same rank and retrieved almost the same wavefield as the numerical modeling. This is crucial information for designing seismic experiments using the MDD-based approach.

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“…The emergence of seismic interferometry theory (e.g. Wapenaar, 2003Wapenaar, , 2004Campillo and Paul, 2003;van-Manen et al, 2005van-Manen et al, , 2006van-Manen et al, , 2007Wapenaar and Fokkema, 2006;Slob et al, 2007;Curtis et al, 2009;Curtis and Halliday, 2010a,b;Wapenaar et al, 2011) allowed us to decode the information contained in the ambient noise wavefield to create a useful signal, in fact an artificial seismogram, from what used to be called noise. This new seismogram can then be used to image the subsurface of the Earth using traditional seismological tomographic or imaging methods.…”

mentioning

confidence: 99%

Seismic interferometry and ambient noise tomography in the British Isles

Nicolson

Curtis

Baptie

et al. 2012

Proceedings of the Geologists' Association

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Improved surface-wave retrieval from ambient seismic noise by multi-dimensional deconvolution

Cited by 42 publications

References 22 publications

Application of seismic interferometry by multidimensional deconvolution to ambient seismic noise recorded in Malargüe, Argentina

Application of seismic interferometry by multidimensional deconvolution to ambient seismic noise recorded in Malargüe, Argentina

Singular-value decomposition analysis of source illumination in seismic interferometry by multidimensional deconvolution

Seismic interferometry and ambient noise tomography in the British Isles

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