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Neural Network Least Squares MigrationNormal access

Authors: Z. Liu and G. Schuster
Event name: First EAGE/SBGf Workshop on Least-Squares Migration
Session: Theory and Implementation II 
Publication date: 27 November 2018
DOI: 10.3997/2214-4609.201802936
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 987.67Kb )
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 basis 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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