A GPU implementation of 2nd acoustic least-squares reverse time migration
2
Issued Date
2024
Copyright Date
2021
Resource Type
Language
eng
File Type
application/pdf
No. of Pages/File Size
xi, 67 leaves: ill.
Access Rights
open access
Rights
ผลงานนี้เป็นลิขสิทธิ์ของมหาวิทยาลัยมหิดล ขอสงวนไว้สำหรับเพื่อการศึกษาเท่านั้น ต้องอ้างอิงแหล่งที่มา ห้ามดัดแปลงเนื้อหา และห้ามนำไปใช้เพื่อการค้า
Rights Holder(s)
Mahidol University
Bibliographic Citation
Thesis (M.Sc. (Physics))--Mahidol University, 2021
Suggested Citation
Phudit Sombutsirinun A GPU implementation of 2nd acoustic least-squares reverse time migration. Thesis (M.Sc. (Physics))--Mahidol University, 2021. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/99499
Title
A GPU implementation of 2nd acoustic least-squares reverse time migration
Author(s)
Advisor(s)
Abstract
Least-squares reverse time migration (LSRTM) is a seismic imaging method that can provide higher-resolution images of the subsurface structures compared to other methods. However, LSRTM is computationally expensive. To reduce the computational time of LSRTM, GPU can be utilized. This leads to the objective of this work which is to develop a GPU implementation of LSRTM. In this work, the two-dimensional first-order acoustic wave equations were solved using the second-order finite difference method on a staggered grid, and a perfectly matched layer was used as an absorbing boundary condition. The adjoint-state method was used to compute the gradient of the objective function concerning model parameters. A linear conjugate gradient method was used to minimize the objective function. Both forward- and backward-propagation of wavefields using the finite-difference method were performed on a single GPU using the NVIDIA CUDA library. For verification purposes, the GPU program of LSRTM was applied to a synthetic data set generated from the Marmousi model. Numerical results showed that LSRTM could provide an image with higher quality compared to a conventional RTM image. The GPU-version of LSRTM has acquired a speedup factor of 12-to-13 times compared to the serial CPU-version of LSRTM.
Description
Physics (Mahidol University 2021)
Degree Name
Master of Science
Degree Level
Master's degree
Degree Department
Faculty of Science
Degree Discipline
Physics
Degree Grantor(s)
Mahidol University