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Neural Network Least Squares MigrationNormal access

Authors: Z. Liu and G. Schuster
Event name: 81st EAGE Conference and Exhibition 2019
Session: Least-Squares RTM
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201900831
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 1.28Mb )
Price: € 20

Summary:
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.


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