Improved time-lapse data repeatability with randomized sampling and distributed compressive sensing

TitleImproved time-lapse data repeatability with randomized sampling and distributed compressive sensing
Publication TypeConference
Year of Publication2017
AuthorsFelix Oghenekohwo, Felix J. Herrmann
Conference NameEAGE Annual Conference Proceedings
Month06
Keywordscalibration, Compressive Sensing, EAGE, noise, repeatability, time lapse
Abstract

Recently, new ideas on randomized sampling for time-lapse seismic acquisition have been proposed to address some of the challenges of replicating time-lapse surveys. These ideas, which stem from distributed compressed sensing (DCS) led to the birth of a joint recovery model (JRM) for processing time-lapse data (noise-free) acquired from non-replicated acquisition geometries. However, when the earth does not change–-i.e. no time-lapse—the recovered vintages from two non-replicated surveys should show high repeatability measured in terms of normalized RMS, which is a standard metric for quantifying time-lapse data repeatability. Under this assumption of no time-lapse change, we demonstrate improved repeatability (with JRM) of the recovered data from non-replicated random samplings, first with noisy data and secondly in situations where there are calibration errors i.e. where the acquisition parameters such as source/receiver coordinates are not precise.

Notes

(EAGE, Paris)

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Conferences/EAGE/2017/oghenekohwo2017EAGEitl/oghenekohwo2017EAGEitl.html
DOI10.3997/2214-4609.201701389
Presentation

https://www.slim.eos.ubc.ca/Publications/Public/Conferences/EAGE/2017/og...

Citation Keyoghenekohwo2017EAGEitl