TY - GEN
T1 - Neural network least squares migration
AU - Liu, Z.
AU - Schuster, Gerard T.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2019/8/26
Y1 - 2019/8/26
N2 - Sparse least squares migration (SLSM) estimates the reflectivity distribution that honors a sparsity condition. This problem can be reformulated by finding both the sparse coefficients and basics functions from the data to predict the migration image. This is designated as neural network least squares migration (NLSM), which is a more general formulation of SLSM. This reformulation opens up new thinking for improving SLSM by adapting ideas from the machine learning community.
AB - Sparse least squares migration (SLSM) estimates the reflectivity distribution that honors a sparsity condition. This problem can be reformulated by finding both the sparse coefficients and basics functions from the data to predict the migration image. This is designated as neural network least squares migration (NLSM), which is a more general formulation of SLSM. This reformulation opens up new thinking for improving SLSM by adapting ideas from the machine learning community.
UR - http://hdl.handle.net/10754/661855
UR - http://www.earthdoc.org/publication/publicationdetails/?publication=97490
UR - http://www.scopus.com/inward/record.url?scp=85085852776&partnerID=8YFLogxK
U2 - 10.3997/2214-4609.201900831
DO - 10.3997/2214-4609.201900831
M3 - Conference contribution
SN - 9789462822894
BT - 81st EAGE Conference and Exhibition 2019
PB - EAGE Publications BV
ER -