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

Data sets

pytzcarraldo/uninterp: Paper: Quantification of uncertainty in 3-D seismic interpretation (Version paper_SE) A. Schaaf https://doi.org/10.5281/zenodo.2593587

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.