Accounting for Processing Errors in AVO/AVA Data

Rasmus Bødker Madsen; Egon  Nørmark; Thomas Mejer Hansen

doi:10.3997/2214-4609.201801347

Accounting for Processing Errors in AVO/AVA Data

Rasmus Bødker Madsen, Egon Nørmark, Thomas Mejer Hansen

Klima- og Geofysik / Climate and Geophysics

1 Citationer (Scopus)

Abstract

Processing of raw seismic data into AVO/AVA data serves many purposes, but also induces some unwanted features (errors) in the resulting data set. Here we study the effect of such processing in an idealized case with a synthetic raw data set. The behavior of the processing errors are estimated using a statistical Gaussian model. The 1D marginal distribution of this model show a good match with observed errors. The subsequent linearized inversion reveals that the processing errors can only be safely ignored for a signal-to-noise ratio (S/N) of 0,4 or below when using an uncorrelated noise model. Such inversion results will have poor posterior resolution. Uncorrelated models with a higher S/N will be biased. Using the estimated Gaussian model to describe the noise in the data eliminates this bias and increases resolution in linear inversion. In a real-world case we expect the threshold of 0.4 to be even lower.

Originalsprog	Dansk
Publikationsdato	1 jun. 2018
Antal sider	6
DOI	https://doi.org/10.3997/2214-4609.201801347
Status	Udgivet - 1 jun. 2018
Begivenhed	80th EAGE Annual Conference and Exhibition 2018 - Bella Center, København, Danmark Varighed: 10 jun. 2018 → 15 jun. 2018 https://events.eage.org/en/2018/eage-annual-2018

Konference

Konference	80th EAGE Annual Conference and Exhibition 2018
Lokation	Bella Center
Land/Område	Danmark
By	København
Periode	10/06/2018 → 15/06/2018
Internetadresse	https://events.eage.org/en/2018/eage-annual-2018

Adgang til dokumentet

10.3997/2214-4609.201801347

Madsen et al., 2018 - Accounting for Processing Errors in AVO-AVA Data-pdfForlagets udgivne version, 7,15 MB

Citationsformater

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abstract = "Processing of raw seismic data into AVO/AVA data serves many purposes, but also induces some unwanted features (errors) in the resulting data set. Here we study the effect of such processing in an idealized case with a synthetic raw data set. The behavior of the processing errors are estimated using a statistical Gaussian model. The 1D marginal distribution of this model show a good match with observed errors. The subsequent linearized inversion reveals that the processing errors can only be safely ignored for a signal-to-noise ratio (S/N) of 0,4 or below when using an uncorrelated noise model. Such inversion results will have poor posterior resolution. Uncorrelated models with a higher S/N will be biased. Using the estimated Gaussian model to describe the noise in the data eliminates this bias and increases resolution in linear inversion. In a real-world case we expect the threshold of 0.4 to be even lower.",

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