2018
DOI: 10.1007/s11770-018-0681-x
|View full text |Cite
|
Sign up to set email alerts
|

Least squares reverse-time migration in the pseudodepth domain and reservoir exploration

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 8 publications
(2 citation statements)
references
References 12 publications
0
2
0
Order By: Relevance
“…Plessix (2013) introduced the concept of the pseudo-depth domain into FWI, which can effectively avoid the ambiguity caused by velocity depth in VTI media. Li et al (2017) and Sun et al (2018) proposed a pseudodepth domain cross-correlation least-squares inverse time migration method, which weakens the ambiguity of velocity and depth in traditional least-squares inverse time migration, improves the fault tolerance, computational efficiency, and reduces the memory occupied.…”
Section: Introductionmentioning
confidence: 99%
“…Plessix (2013) introduced the concept of the pseudo-depth domain into FWI, which can effectively avoid the ambiguity caused by velocity depth in VTI media. Li et al (2017) and Sun et al (2018) proposed a pseudodepth domain cross-correlation least-squares inverse time migration method, which weakens the ambiguity of velocity and depth in traditional least-squares inverse time migration, improves the fault tolerance, computational efficiency, and reduces the memory occupied.…”
Section: Introductionmentioning
confidence: 99%
“…In the context of RTM, the least‐squares reverse time migration (LSRTM) has been developed to improve imaging quality based on two‐way wave equation (Schuster, 1993; Yao & Wu, 2015; Li et al., 2017; Liu et al., 2017; Li et al., 2021). With the rapid development of computer technology, including the widespread use of multi‐node parallel computer technology, LSRTM based on two‐way wave equation has gradually been applied to field and synthetic data imaging (Dong et al., 2012; Zhang et al., 2015; Hou et al., 2016; Wu et al., 2016; Chen et al., 2017; Li et al., 2017b; Sun et al., 2018; Yao et al., 2022). The LSRTM advantages include the enhancement of imaging resolution and the dramatic minimization of imaging artefacts in comparison to RTM.…”
Section: Introductionmentioning
confidence: 99%