Converted-Wave Seismic Reflection as an Alternative Key to Unlock Near Surface Imaging

Oktariena, Madaniya; Triyoso, Wahyu

doi:10.3997/2214-4609.201800416

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, shallow seismic exploration has attracted increasing attention (Liu, et al ., 2016; Feigenbaum, et al ., 2017; Oktariena and Triyoso, 2018; Wo, et al ., 2018; Zheng, 2018; Gallais, et al ., 2019; Mahvelati, et al ., 2019; Shakuro, 2019; Song, et al ., 2019; Wang, 2019; Zhenbo, et al ., 2019; Sun, et al ., 2020). Near‐surface structures are complex, and the seismic wave fields are rich and variable, which cause difficulties for seismic migration (Almuhaidib and Toksöz, 2014; Krohn and Murray, 2016; Dehghannejad, et al ., 2017; Alam, 2019).…”

Section: Introductionmentioning

confidence: 99%

A numerical dispersion‐suppressed method for shallow seismic migration

Wang

et al. 2020

Near Surface Geophysics

View full text Add to dashboard Cite

Reverse time migration can accurately image underground earth structures. However, for shallow seismic exploration, the seismic wave velocity is often lower than the velocity in the middle‐deep layers, which causes numerical dispersion for finite‐difference schemes and leads to poor seismic imaging quality. Suppressing numerical dispersion by grid encryption or increasing the finite‐difference order seriously reduces computational efficiency, which is not the optimal solution. To improve the imaging quality without sacrificing computational efficiency, a regularization factor is added to the acoustic wave equation to correct the phase velocity of high wave‐number components. An appropriate regularization factor can eliminate numerical dispersion that results from large grid interval schemes and can reduce the size of computational grids and improve computational efficiency. For simulations in the frequency domain, reducing the grid size also means reducing computer memory requirements. Numerical experiments indicate that the regularization factor should match the degree of numerical dispersion. Larger regularization factors can suppress serious numerical dispersion. However, excessively large regularization factors may destroy the effective wave field. Different numerical models verify the effectiveness of the improved acoustic equation for suppressing numerical dispersion and maintaining amplitudes and provide a novel means to improve the shallow seismic image quality.

show abstract

Section: Introductionmentioning

confidence: 99%

A numerical dispersion‐suppressed method for shallow seismic migration

Wang

et al. 2020

Near Surface Geophysics

View full text Add to dashboard Cite

show abstract

Converted-Wave Seismic Reflection as an Alternative Key to Unlock Near Surface Imaging

Cited by 1 publication

References 0 publications

A numerical dispersion‐suppressed method for shallow seismic migration

A numerical dispersion‐suppressed method for shallow seismic migration

Contact Info

Product

Resources

About