Accounting for Processing Errors in AVO/AVA Data

Rasmus Bødker Madsen; Egon Nørmark; Thomas Mejer Hansen

Accounting for Processing Errors in AVO/AVA Data

Rasmus Bødker Madsen
, Egon Nørmark
, Thomas Mejer Hansen

Department of Geoscience

Research output: Contribution to conference › Conference abstract for conference › Research › peer-review

3 Citations (Scopus)

1 Downloads (Pure)

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.

Original language	English
Publication date	Mar 2018
Publication status	Published - Mar 2018
Event	80th EAGE Conference and Exhibition - Copenhagen, Denmark Duration: 11 Jun 2018 → 15 Jun 2018

Conference

Conference	80th EAGE Conference and Exhibition
Country/Territory	Denmark
City	Copenhagen
Period	11/06/2018 → 15/06/2018

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title = "Accounting for Processing Errors in AVO/AVA Data",

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.",

author = "Madsen, \{Rasmus B{\o}dker\} and Egon N{\o}rmark and Hansen, \{Thomas Mejer\}",

year = "2018",

month = mar,

language = "English",

note = "80th EAGE Conference and Exhibition ; Conference date: 11-06-2018 Through 15-06-2018",

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TY - ABST

T1 - Accounting for Processing Errors in AVO/AVA Data

AU - Madsen, Rasmus Bødker

AU - Nørmark, Egon

AU - Hansen, Thomas Mejer

PY - 2018/3

Y1 - 2018/3

N2 - 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.

AB - 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.

M3 - Conference abstract for conference

T2 - 80th EAGE Conference and Exhibition

Y2 - 11 June 2018 through 15 June 2018

ER -

Accounting for Processing Errors in AVO/AVA Data

Abstract

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