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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Wenzel on behalf of the Authors (11 Jun 2019)  Author's response
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)
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.