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NARROW
GeoRef Subject
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all geography including DSDP/ODP Sites and Legs
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Arctic Ocean
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Norwegian Sea
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Haltenbanken (1)
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Atlantic Ocean
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North Atlantic
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Gulf of Mexico (1)
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North Sea (4)
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South Atlantic
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Santos Basin (1)
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Campos Basin (1)
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South America
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Brazil (1)
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commodities
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oil and gas fields (1)
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petroleum (3)
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Primary terms
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Arctic Ocean
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Norwegian Sea
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Haltenbanken (1)
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Atlantic Ocean
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North Atlantic
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Gulf of Mexico (1)
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North Sea (4)
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South Atlantic
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Santos Basin (1)
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data processing (13)
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geophysical methods (17)
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oil and gas fields (1)
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petroleum (3)
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sedimentary rocks
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carbonate rocks (1)
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South America
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Brazil (1)
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sedimentary rocks
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sedimentary rocks
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carbonate rocks (1)
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Enhancing subsalt imaging through advanced identification and suppression of interbed multiples and mode-converted reflections — Gulf of Mexico and Brazil case studies
Full wavefield least-squares reverse time migration
Closed-loop surface-related multiple estimation with full-wavefield migration-reconstructed near offsets for shallow water
Fast local primary-and-multiple orthogonalization for surface-related multiple leakage estimation and extraction
Marchenko multiple elimination and full-wavefield migration in a resonant pinch-out model
Training deep networks with only synthetic data: Deep-learning-based near-offset reconstruction for (closed-loop) surface-related multiple estimation on shallow-water field data
Simultaneous joint migration inversion with calendar-time constraints as a processing tool for semi-continuous surveys
Local primary-and-multiple orthogonalization for leaked internal multiple crosstalk estimation and attenuation on full-wavefield migrated images
Surface-related multiple leakage extraction using local primary-and-multiple orthogonalization
Joint deblending and data reconstruction with focal transformation
Full-waveform inversion and joint migration inversion with an automatic directional total variation constraint
Integrated receiver deghosting and closed-loop surface-multiple elimination
Enriched seismic imaging by using multiple scattering
Turning noise into geologic information: The next big step?—A joint EAGE/SEG Forum
Seismic migration of blended shot records with surface-related multiple scattering
A perspective on 3D surface-related multiple elimination
Abstract Surface-related multiple elimination (SRME) is an algorithm that predicts all surface multiples by a convolutional process applied to seismic field data. Only minimal preprocessing is required. Once predicted, the multiples are removed from the data by adaptive subtraction. Unlike other methods of multiple attenuation, SRME does not rely on assumptions or knowledge about the subsurface, nor does it use event properties to discriminate between multiples and primaries. In exchange for this “freedom from the subsurface,” SRME requires knowledge of the acquisition wavelet and a dense spatial distribution of sources and receivers. Although a 2D version of SRME sometimes suffices, most field data sets require 3D SRME for accurate multiple prediction. All implementations of 3D SRME face a serious challenge: The sparse spatial distribution of sources and receivers available in typical seismic field data sets does not conform to the algorithmic requirements. There are several approaches to implementing 3D SRME that address the data sparseness problem. Among those approaches are pre-SRME data interpolation, on-the-fly data interpolation, zero-azimuth SRME, and true-azimuth SRME. Field data examples confirm that (1) multiples predicted using true-azimuth 3D SRME are more accurate than those using zero-azimuth 3D SRME and (2) on-the-fly interpolation produces excellent results.