dGB Earth Sciences


In today’s post we share an elegant workflow proposed by Roar Heggland of Equinor ASA to compute Fault Dip and Fault Azimuth for Machine Learning predicted Fault Likelihood (ML-FL).

Fault Dip and Azimuth for Machine Learning Fault Likelihood

ML-FL produces Fault Likelihood in a very fast and easy way. It is a one-click workflow in OpendTect’s Machine Learning solution. To compute ML-FL you simply select the pre-trained Unet of shape 128x128x128 from the library of pre-trained models and apply this to your 3D seismic volume. ML-FL is often cleaner than Fault Likelihood computed with the original algorithm. However, whereas Fault Likelihood also outputs Fault Dip and Fault Azimuth, ML-FL only outputs Fault Likelihood itself. This creates a problem for OpendTect’s Fault Thinning algorithm and for Automatic Fault plane Extraction. Both algorithms in the Faults & Fractures plugin need all three components as input.

To compute the missing components, Roar simply applies the Fault Likelihood algorithm to a new volume: “1 minus ML-FL”. The idea behind this is that ML-FL separates faults and none faults much better than a seismic volume does. Therefore, we expect better hits in the Fault Likelihood scanning process with ML-FL input than with seismic input. We input “1 minus ML-FL” because faults in seismic data correspond to low semblance values, not high values as in ML-FL.

The slider shows an example of ML-FL, Thinned ML-FL and Automatic Fault planes Extracted from ML-FL. The missing Dip and Azimuth components for ML-FL were computed with the workflow described above.