Quantification of uncertainty in 3-D seismic interpretation: implications for deterministic and stochastic geomodeling and machine learning

Schaaf, Alexander; Bond, Clare E.

doi:https://doi.org/10.5194/se-10-1049-2019

Articles | Volume 10, issue 4

https://doi.org/10.5194/se-10-1049-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Understanding the unknowns: the impact of uncertainty in the...

https://doi.org/10.5194/se-10-1049-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 10, issue 4

Research article

|

05 Jul 2019

Research article |

| 05 Jul 2019

Quantification of uncertainty in 3-D seismic interpretation: implications for deterministic and stochastic geomodeling and machine learning

Alexander Schaaf and Clare E. Bond

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Final revised paper (published on 05 Jul 2019)
Preprint (discussion started on 02 Apr 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review report: Se_2019_54', Billy Andrews, 04 Apr 2019
- AC1: 'Author's Response to RC1', Alexander Schaaf, 06 Jun 2019
RC2: 'Quantification of uncertainty in 3-D seismic interpretation: implications for deterministic and stochastic geomodelling and machine learning', Awad Bilal, 03 May 2019
- AC2: 'Author's Response to RC2', Alexander Schaaf, 06 Jun 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Alexander Schaaf on behalf of the Authors (10 Jun 2019) Manuscript

ED: Publish subject to technical corrections (11 Jun 2019) by Lucia Perez-Diaz

ED: Publish subject to technical corrections (12 Jun 2019) by Federico Rossetti (Executive editor)

AR by Alexander Schaaf on behalf of the Authors (19 Jun 2019) Manuscript

Short summary

Seismic reflection data allow us to infer subsurface structures such as horizon and fault surfaces. The interpretation of this indirect data source is inherently uncertainty, and our work takes a first look at the scope of uncertainties involved in the interpretation of 3-D seismic data. We show how uncertainties of fault interpretations can be related to data quality and discuss the implications for the 3-D modeling of subsurface structures derived from 3-D seismic data.