Neural network least squares migration

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish (US)
Title of host publication81st EAGE Conference and Exhibition 2019
PublisherEAGE Publications BV
ISBN (Print)9789462822894
DOIs
StatePublished - Aug 26 2019

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