Towards reproducing seismic interpretation uncertainties using open-source stochastic geomodeling in Python

Seismic data plays a key role in developing our understanding of the subsurface by providing 2-D and 3-D indirect imaging. But the resulting data needs to be interpreted by specialists using time-intensive, error-prone and subjective manual labour. While the automation of data classification using Machine Learning algorithms is starting to show promising results in areas of good data quality, the classification of noisy and ambiguous data will continue to require geological reasoning for the foreseeable future. In Schaaf & Bond (2019) we provided a first quantification of the uncertainties involved in the structural interpretation of a 3-D seismic volume by analysing 78 student interpretations of the Gullfaks field in the northern North Sea. Our work also concretized the question of to which degree the seismic data itself could provide useful information towards a prediction of interpretation uncertainty.

We now look at the same dataset in an effort to answer the question if we can adequately reproduce the observed interpretation uncertainties by approximating them as aleatoric uncertainties in a stochastic geomodeling framework. For this we make use of the Python-based open-source 3-D implicit structural geomodeling software GemPy to leverage open-source probabilistic programming frameworks and to allow for scientific reproducibility of our results. We identify potential shortcomings of collapsing interpretation uncertainties into aleatoric uncertainties and present ideas on how to improve stochastic parametrization based on the seismic data at hand.

 

Schaaf, A., & Bond, C. E. (2019). Quantification of uncertainty in 3-D seismic interpretation: Implications for deterministic and stochastic geomodeling and machine learning. Solid Earth, 10(4), 1049–1061. https://doi.org/10.5194/se-10-1049-2019