An efficient and accurate finite-difference operator using adaptively discretized grids and its application for 3D least
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ORIGINAL PAPER
An efficient and accurate finite-difference operator using adaptively discretized grids and its application for 3D least-squares reverse-time migration Ziying Wang 1 & Jianping Huang 1 & Zhenchun Li 1 & Dingjin Liu 2 & Peng Yong 1 Received: 9 March 2019 / Accepted: 1 May 2020 / Published online: 15 June 2020 # Saudi Society for Geosciences 2020
Abstract Least-square reverse-time migration (LSRTM) is a powerful tool to image subsurface reflectivity with high resolution. It has the ability to reduce migration artifacts, balance amplitudes, and improve imaging resolution. However, the large amount of computation cost is one of its challenging problems, especially for 3D problems. We propose an efficient and accurate finitedifference modeling operator using an adaptive variable grid strategy. The resampled model’s grid intervals adapt to local velocity and wave frequency, ensuring that dispersion is mitigated to some extent. Furthermore, we apply the modeling operator to 3D LSRTM with graphics processing unit (GPU) implementation in order to mitigate the large calculation costs. The 3D modeling is applied to two synthetic examples to validate its feasibility, accuracy, and efficiency. The imaging results of the 3D SEG/EAGE overthrust model demonstrated that adaptive grid LSRTM (AGLSRTM) is capable of reducing computing time and memory requirement while producing the same imaging accuracy as traditional LSRTM. Keywords Adaptive grid . 3D finite-difference modeling operator . Least-square reverse-time migration . GPU implementation
Introduction As 3D seismic surveys have been widely implemented, 3D high-resolution imaging techniques are needed in geophysical exploration. Although reverse-time migration (RTM) is capable of imaging complicated underground structures (Baysal et al. 1983; McMechan 1983), it still has drawbacks such as migration artifacts and un-balanced amplitudes. The idea of least-squares migration (LSM) was firstly applied to Kirchhoff migration (Nemeth et al. 1999; Huang et al. 2013; Liu et al. 2015). Then, it is extended to beam migration (Hu et al. 2016; Yang et al. 2018) and one-way wave-equation migration (Kühl and Sacchi 2003) to improve imaging quality. LSM iteratively finds a reflectivity model by using optimization method to implement the best matching between Responsible Editor: Lun Li * Jianping Huang [email protected] 1
School of Geosciences, China University of Petroleum, Qingdao 266580, China
2
Sinopec Geophysical Research Institute, Nanjing 211103, China
synthetic and observed data. Recently, the LSM has been extended to RTM, which is known as least-squares reversetime migration (LSRTM) (Dai et al. 2012; Dong et al. 2012; Yang et al. 2019). Compared with traditional adjoint migration, LSRTM has the ability to reduce migration artifacts, balance amplitudes, and improve imaging resolution (Yang and Zhu 2019). Due to the significant development of computational efficiency, LSRTM has been widely used in seismic imaging because of its advantages (Li et al. 2017a, b). Howev
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