2020
DOI: 10.1007/s12517-020-05417-4
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An efficient and accurate finite-difference operator using adaptively discretized grids and its application for 3D least-squares reverse-time migration

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Cited by 7 publications
(5 citation statements)
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“…There are two schemes can be considered: First, we can use finite‐difference method to replace the hybrid finite‐difference/pseudo‐spectral method for forward modelling. Second, we can adopt dual‐variable grid forward modelling method to reduce computation cost, which can be realized by matching the size variability of grid spacing using grid spacing of different sizes in different regions and locally variable time steps (Huang et al., 2015; Wang et al., 2020).…”
Section: Discussionmentioning
confidence: 99%
“…There are two schemes can be considered: First, we can use finite‐difference method to replace the hybrid finite‐difference/pseudo‐spectral method for forward modelling. Second, we can adopt dual‐variable grid forward modelling method to reduce computation cost, which can be realized by matching the size variability of grid spacing using grid spacing of different sizes in different regions and locally variable time steps (Huang et al., 2015; Wang et al., 2020).…”
Section: Discussionmentioning
confidence: 99%
“…where v min (z) denotes the minimum velocity along the depth axis z, f m is the dominant frequency of the source wavelet, and dz(z) denotes the optimal vertical grid spacing. We use a rectangular sampling method (Wang et al, 2020) to resample the initial migration model. Firstly, we set a small trial step from z 0 and increase it continuously to get the first grid point η 1 , where η 1 dz(η 1 ).…”
Section: Adaptive Sampling Strategymentioning
confidence: 99%
“…Li et al (2014) developed a dual-variable grid algorithm and applied it to RTM. Li et al (2017) introduced the idea of pseudo-time domain (Alkhalifah, 2003;Ma and Alkhalifah, 2013) into LSRTM, which improves imaging efficiency by reducing vertical grid points (Wang et al, 2020). Proposed an adaptive grid discretization strategy and applied it to 3D LSRTM.…”
Section: Introductionmentioning
confidence: 99%
“…Gu et al (2015) developed a modified imaging condition of excitation amplitude prestack RTM, which saves memory consumption on the premise of ensuring accuracy. Wang et al (2019); Wang et al (2020) combined the variable grid method with the GPU parallel strategy and introduced it into FWI and LSRTM, which effectively improves the efficiency of the computation.…”
Section: Introductionmentioning
confidence: 99%