A new imaging condition for least-squares reverse time migration with local similarity

2018 ◽  
Author(s):  
Young Seo Kim ◽  
Ali Almomin ◽  
Woodon Jeong ◽  
Constantine Tsingas
Keyword(s):  
Least Squares ◽  
Local Similarity ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Imaging Condition ◽  
Time Migration

scholarly journals An adaptive unstructured mesh based solution to topography least-squares reverse-time imaging

2019 ◽  
Author(s):  
Qiancheng Liu ◽  
Jianfeng Zhang
Keyword(s):  
Least Squares ◽  
Unstructured Mesh ◽  
Voronoi Tessellation ◽  
Grid Method ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Imaging Condition ◽  
Time Migration ◽  
Rugged Topography ◽  
Backward Propagation

Abstract. Least-squares reverse-time migration (LSRTM) attempts to invert for the broadband-wavenumber reflectivity image by minimizing the residual between observed and predicted seismograms via linearized inversion. However, rugged topography poses a challenge in front of LSRTM. To tackle this issue, we present an unstructured mesh-based solution to topography LSRTM. As to the forward/adjoint modeling operators in LSRTM, we take a so-called unstructured mesh-based “grid method”. Before solving the two-way wave equation with the grid method, we prepare for it a velocity-adaptive unstructured mesh using a Delaunay Triangulation plus Centroidal Voronoi Tessellation (DT-CVT) algorithm. The rugged topography acts as constraint boundaries during mesh generation. Then, by using the adjoint method, we put the observed seismograms to the receivers on the topography for backward propagation to produce the gradient through the cross-correlation imaging condition. We seek the inverted image using the conjugate gradient method during linearized inversion to linearly reduce the data misfit function. Through the 2D SEG Foothill synthetic dataset, we see that our method can handle the LSRTM from rugged topography.

Preconditioned least-squares reverse time migration with energy imaging condition

2017 ◽  
Author(s):  
Mengli Liu ◽  
Jianping Huang ◽  
Chuang Li ◽  
Yingjun Ren ◽  
Zhenchun Li
Keyword(s):  
Least Squares ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Imaging Condition ◽  
Time Migration

Elastic least-squares reverse time migration

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. S315-S325 ◽  
Author(s):  
Yuting Duan ◽  
Antoine Guitton ◽  
Paul Sava
Keyword(s):  
Least Squares ◽  
Reservoir Characterization ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Imaging Condition ◽  
Time Migration ◽  
Energy Focusing ◽  
Polarity Reversals ◽  
New Perturbation ◽  
Least Squares Migration

Least-squares migration can produce images with improved resolution and reduced migration artifacts, compared with conventional imaging. We have developed a method for elastic least-squares reverse time migration (LSRTM) based on a new perturbation imaging condition that yields scalar images of squared P- and S-velocity perturbations. These perturbation images do not suffer from polarity reversals that are common for more conventional elastic imaging methods. We use 2D synthetic and field-data examples to demonstrate the proposed LSRTM algorithm using the perturbation imaging condition. Our results show that elastic LSRTM improves the energy focusing and illumination of the elastic images and it attenuates artifacts resulting, for instance, from sparseness in the wavefield sampling and crosstalk of the P- and S-modes. Compared with RTM images, the LSRTM images provide more accurate relative amplitude information that is useful for reservoir characterization.

scholarly journals A GPU implementation of least-squares reverse time migration

2021 ◽  
Vol 1719 (1) ◽  
pp. 012030
Author(s):  
Phudit Sombutsirinun ◽  
Chaiwoot Boonyasiriwat
Keyword(s):  
Least Squares ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Gpu Implementation
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