Ultra-high density land nodal seismic – Processing challenges and rewards

Buriola, F.; Mills, Keith; Cooper, S. J.; Hollingworth, S.; Crosby, Alfred J.; Ourabah, Amine

doi:10.3997/2214-4609.202112916

Cited by 3 publications

(4 citation statements)

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“…To further understand the results we compare the above shear velocity models with P-wave velocity model (Figure 6) obtained using refraction tomography from the same dataset [5]. At 20 m depth the Vp model shows similar features to the shear velocity models.…”

Section: D Resultsmentioning

confidence: 85%

“…The increased trace density greatly improves the spatial sampling of the wavefield, which in turn benefits the recording and analysis of surface waves. As a part of the depth model building process, refraction tomography was performed to yield a shallow P-wave velocity model [5], which is then used here for qualitative comparison with the shallow S-wave velocity model obtained from our method.…”

Section: Introductionmentioning

confidence: 99%

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Surface wave dispersion inversion using an energy likelihood function

Zhang,

Zheng,

Curtis

2022

Preprint

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Seismic surface wave dispersion inversion is used widely to study the subsurface structure of the Earth. The dispersion property is usually measured by using frequency-phase velocity (f-c) analysis and by picking phase velocities from the obtained f-c spectrum. However, because of potential contamination the f-c spectrum often has multimodalities at each frequency for each mode. These introduce uncertainty and errors in the picked phase velocities, and consequently the obtained shear velocity structure is biased. To overcome this issue, in this study we introduce a new method which directly uses the spectrum as data. We achieve this by solving the inverse problem in a Bayesian framework and define a new likelihood function, the energy likelihood function, which uses the spectrum energy to define data fit. We apply the new method to a land dataset recorded by a dense receiver array, and compare the results to those obtained using the traditional method. The results show that the new method produces more accurate results since they better match independent data from refraction tomography. This real-data application also shows that it can be applied efficiently since it removes the need to pick phase velocities, and with relatively little adjustment to current practice since it uses standard f-c panels to define the likelihood. We therefore recommend using the energy likelihood function rather than explicitly picking phase velocities in surface wave dispersion inversion.

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Section: D Resultsmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Surface wave dispersion inversion using an energy likelihood function

Zhang,

Zheng,

Curtis

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To further understand the results we compare the above shear velocity models with P-wave velocity model (Figure 8) obtained using refraction tomography from the same dataset (Buriola et al 2021). At 20 m depth the Vp model shows similar features to the shear velocity models.…”

Section: D Resultsmentioning

confidence: 85%

“…The increased trace density greatly improves the spatial sampling of the wavefield, which in turn benefits the recording and analysis of surface waves. As a part of the depth model building process, refraction tomography was performed to yield a shallow P-wave velocity model (Buriola et al 2021), which is then used here for qualitative comparison with the shallow S-wave velocity model obtained from our method.…”

mentioning

confidence: 99%

Surface wave dispersion inversion using an energy likelihood function

Zhang

Zheng

Curtis

2022

Geophysical Journal International

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Summary Seismic surface wave dispersion inversion is used widely to study the subsurface structure of the Earth. The dispersion property is usually measured by using frequency-phase velocity (f-c) analysis of data recorded on a local array of receivers. The apparent phase velocity at each frequency of the surface waves travelling across the array is that at which the f-c spectrum has maximum amplitude. However, because of potential contamination by other wave arrivals or due to complexities in the velocity structure the f-c spectrum often has multiple maxima at each frequency for each mode. These introduce errors and ambiguity in the picked phase velocities, and consequently the estimated shear velocity structure can be biased, or may not account for the full uncertainty in the data. To overcome this issue we introduce a new method which directly uses the spectrum as the data to be inverted. We achieve this by solving the inverse problem in a Bayesian framework and define a new likelihood function, the energy likelihood function, which uses the spectrum energy to define data fit. We apply the new method to a land dataset recorded by a dense receiver array, and compare the results to those obtained using the traditional method. The results show that the new method produces more accurate results since they better match independent data from refraction tomography. This real-data application also shows that it can be applied with relatively little adjustment to current practice since it uses standard f-c panels to define the likelihood, and efficiently since it removes the need to pick phase velocities. We therefore conclude that the energy likelihood function can be a valuable tool for surface wave dispersion inversion in practice.

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