SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces

Michalak, Michał P.; Gerhards, Christian; Menzel, Peter

doi:https://doi.org/10.5194/gmd-18-4469-2025

Articles | Volume 18, issue 14

https://doi.org/10.5194/gmd-18-4469-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-18-4469-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 14

Model description paper

|

23 Jul 2025

Model description paper |

| 23 Jul 2025

SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces

Michał P. Michalak, Christian Gerhards, and Peter Menzel

Data sets

SubsurfaceBreaks v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces: Input and Processed Data Michał Michalak https://doi.org/10.5281/zenodo.14589469

Model code and software

SubsurfaceBreaks Michał Michalak https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/README.md

SubsurfaceBreaks v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces: Input and Processed Data Michał Michalak https://doi.org/10.5281/zenodo.14589469

Short summary

Using geometric features of synthetic triangulated models of subsurface homoclinal interfaces, we applied machine learning to detect faults. Testing on real borehole data validated its effectiveness across various fault orientations. The supervised approach represents a significant improvement over older methods that relied on simpler clustering techniques which were capable of identifying fewer orientations of potential faults.