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 | Danish |
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| Publication date | 1 Jun 2018 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 1 Jun 2018 |
| Event | 80th EAGE Annual Conference and Exhibition 2018 - Bella Center, København, Denmark Duration: 10 Jun 2018 → 15 Jun 2018 https://events.eage.org/en/2018/eage-annual-2018 |
Conference
| Conference | 80th EAGE Annual Conference and Exhibition 2018 |
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| Location | Bella Center |
| Country/Territory | Denmark |
| City | København |
| Period | 10/06/2018 → 15/06/2018 |
| Internet address |