Obtaining local reflectivity at two-way travel time by filtering acoustic reflection data

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

17 Downloads (Pure)

Abstract

A modified implementation of Marchenko redatuming leads to a filter that removes internal multiples from reflection data. It produces local reflectivity at two-way travel time. The method creates new primary reflections resulting from emitted events that eliminate internal multiples. We call these non-physical
primaries and their presence is a disadvantage. The advantage is that the filter is model free. We give the 3D filter and demonstrate with 1D arguments that starting the focusing wavefield with a unit impulse at zero time, while focusing below the bottom reflector, is the choice that leads to a model free implementation. The starting impulse generates the reflection data. Every later emitted pulse eliminates an internal multiple somewhere in the model and helps removing the transmission
amplitude effects in a physical primary. We show that
the amplitude of the non-physical primaries are a product of
three reflections, making them generally smaller than those of
the physical primaries. A 2D modeled shotgather at different
stages of filtering the data shows that the filter works well.
Original languageEnglish
Title of host publicationProceedings of the 87th SEG annual meeting, expanded abstracts
EditorsA. Mihai Popovici, S. Fomel
PublisherSEG
Pages4813-4817
DOIs
Publication statusPublished - 2017
Event87th SEG annual meeting - Houston, United States
Duration: 24 Sep 201729 Sep 2017
Conference number: 87
https://seg.org/Annual-Meeting-2017

Publication series

NameSEG Technical Program Expanded Abstracts 2017
ISSN (Electronic)1949-4645

Conference

Conference87th SEG annual meeting
CountryUnited States
CityHouston
Period24/09/1729/09/17
Internet address

Fingerprint

Dive into the research topics of 'Obtaining local reflectivity at two-way travel time by filtering acoustic reflection data'. Together they form a unique fingerprint.

Cite this