Laterally constrained inversion of ground roll from seismic reflection records

Socco, Laura; Boiero, Daniele; Foti, Sebastiano; Wisén, Roger

doi:10.1190/1.3223636

Cited by 102 publications

(84 citation statements)

References 26 publications

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“…In the adopted inversion procedure, a single initial model was estimated and subsequently used for the deterministic inversion of all dispersion curve data sets (July 2013 to November 2014), following the approach introduced by Socco et al (2009) and Boiero and Socco (2010).…”

Section: Inversion Methodsmentioning

confidence: 99%

“…As suggested by Socco et al (2009), we obtained the uncertainty for Rayleigh-wave dispersion curves from the stacked seismograms (Figure 4) by extracting the curves from the three relevant individual shots (see the "Data acquisition and processing" section): the velocity range identified for each frequency by these dispersion curves defines the uncertainty range of phase velocity. For the purpose of this test, only the experimental uncertainties for the September and November 2014 data sets were computed.…”

Section: Temporal Variability Versus Experimental Uncertaintymentioning

confidence: 99%

“…Considering the regular and dense spatial distribution of dispersion curve data, these were inverted using a laterally constrained inversion (LCI) algorithm. The LCI, originally developed for the inversion of resistivity data (Auken and Christiansen, 2004) and later successfully applied to SW data interpretation (Wisén and Christiansen, 2006;Socco et al, 2009), is a deterministic inversion scheme involving the simultaneous inversion of spatially referenced geophysical data for a set of 1D models. These models are regularized through spatial constraints tying corresponding model parameters.…”

Section: Least-squares Inversionmentioning

confidence: 99%

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Time-lapse monitoring of climate effects on earthworks using surface waves

et al. 2016

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The UK's transportation network is supported by critical geotechnical assets (cuttings/embankments/dams) that require sustainable, cost-effective management, while maintaining an appropriate service level to meet social, economic, and environmental needs. Recent effects of extreme weather on these geotechnical assets have highlighted their vulnerability to climate variations. We have assessed the potential of surface wave data to portray the climate-related variations in mechanical properties of a clay-filled railway embankment. Seismic data were acquired bimonthly from July 2013 to November 2014 along the crest of a heritage railway embankment in southwest England. For each acquisition, the collected data were first processed to obtain a set of Rayleigh-wave dispersion and attenuation curves, referenced to the same spatial locations. These data were then analyzed to identify a coherent trend in their spatial and temporal variability. The relevance of the observed temporal variations was also verified with respect to the experimental data uncertainties. Finally, the surface wave dispersion data sets were inverted to reconstruct a time-lapse model of S-wave velocity for the embankment structure, using a least-squares laterally constrained inversion scheme. A key point of the inversion process was constituted by the estimation of a suitable initial model and the selection of adequate levels of spatial regularization. The initial model and the strength of spatial smoothing were then kept constant throughout the processing of all available data sets to ensure homogeneity of the procedure and comparability among the obtained V S sections. A continuous and coherent temporal pattern of surface wave data, and consequently of the reconstructed V S models, was identified. This pattern is related to the seasonal distribution of precipitation and soil water content measured on site.

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Section: Inversion Methodsmentioning

confidence: 99%

Section: Temporal Variability Versus Experimental Uncertaintymentioning

confidence: 99%

Section: Least-squares Inversionmentioning

confidence: 99%

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Time-lapse monitoring of climate effects on earthworks using surface waves

et al. 2016

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“…We used two vintage data sets for which V S models had been estimated in previous works by inverting the experimental DCs (Socco et al, , 2009). The two DC data sets are extracted from seismic records acquired for high-resolution seismic reflection surveys.…”

Section: Field Examplesmentioning

confidence: 99%

Time-average velocity estimation through surface-wave analysis: Part 1 — S-wave velocity

Socco

Comina²,

Anjom

2017

GEOPHYSICS

Self Cite

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In some areas, the estimation of static corrections for land seismic data is a critical step of the processing workflow. It often requires the execution of additional surveys and data analyses. Surface waves (SWs) in seismic records can be processed to extract local dispersion curves (DCs) that can be used to estimate near-surface S-wave velocity models. Here we focus on the direct estimation of time-average S-wave velocity models from SW DCs without the need to invert the data. Time-average velocity directly provides the value of one-way time, given a datum plan depth. The method requires the knowledge of one 1D S-wave velocity model along the seismic line, together with the relevant DC, to estimate a relationship between SW wavelength and investigation depth on the time-average velocity model. This wavelength/depth relationship is then used to estimate all the other time-average S-wave velocity models along the line directly from the DCs by means of a data transformation. This approach removes the need for extensive data inversion and provides a simple method suitable for industrial workflows. We tested the method on synthetic and field data and found that it is possible to retrieve the time-average velocity models with uncertainties less than 10% in sites with laterally varying velocities. The error on one-way times at various depths of the datum plan retrieved by the time-average velocity models is mostly less than 5 ms for synthetic and field data.

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“…2 Different forms of dispersion analysis have been proposed for estimating S-wave velocitydepth profiles for a large variety of applications (e.g., Gabriels et al, 1987;Bitri et al, 1998;Grandjean and Bitri, 2006;Socco et al, 2009). …”

Section: Introductionmentioning

confidence: 99%

Surface wave interferometry by crosscorrelation and deconvolution

Bitri¹,

Grandjean²,

Samyn³

2011

First Break

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Frequency analysis of surface waves propagation is an efficient tool to retrieve the vertical shear-wave velocity profile. Surface waves result from the interaction of elastic body waves with a free surface. They are commonly generated by impulsive active sources but are also present in ambient noise. Here we compare the results of interferometric estimation of surface-wave dispersion from controlled-noise sources by using both cross-correlation and deconvolution methods. The crucial step in both methods lies in the dispersion image computation that conditions the reliability of the dispersion curve, and therefore the inversion results. We have found that the dispersion characteristics of surface waves are better defined when the deconvolution method is used. This better definition is probably due to the relative independence of the deconvolved output from the source function.

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Laterally constrained inversion of ground roll from seismic reflection records

Cited by 102 publications

References 26 publications

Time-lapse monitoring of climate effects on earthworks using surface waves

Time-lapse monitoring of climate effects on earthworks using surface waves

Time-average velocity estimation through surface-wave analysis: Part 1 — S-wave velocity

Surface wave interferometry by crosscorrelation and deconvolution

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