Preprints
https://doi.org/10.5194/egusphere-2024-2004
https://doi.org/10.5194/egusphere-2024-2004
08 Jul 2024
 | 08 Jul 2024
Status: this preprint is open for discussion.

Broken Terrains v. 1.0: A supervised detection of fault-related lineaments on geological terrains

Michał Michalak, Christian Gerhards, and Peter Menzel

Abstract. The study presents a novel approach for fault detection on geological terrains using supervised learning algorithm and careful variable selection. Synthetic faulted terrains are generated using Delaunay triangulation via the Computational Geometry Algorithms Library (CGAL) allowing for adjustments of parameters. We introduce 24 variables, including local geometric features and neighborhood analysis, for classification. Support Vector Machine (SVM) is employed as the classification algorithm, achieving high precision and recall rates for fault-related observations. Application to real borehole data demonstrates the effectiveness of the method in detecting fault orientations, the challenges remain with respect to distinguishing faults with opposite dip directions. The study highlights the need to address 3D fault zone complexities and their identification. Despite limitations, the proposed supervised approach offers significant advancement over clustering-based methods, showing promise in detecting faults of various orientations. Future research directions include exploring more complex geological scenarios and refining fault detection methodologies.

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Michał Michalak, Christian Gerhards, and Peter Menzel

Status: open (until 02 Sep 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Michał Michalak, Christian Gerhards, and Peter Menzel

Model code and software

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

Michał Michalak, Christian Gerhards, and Peter Menzel

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Short summary
This study presents a novel method for fault detection on geological terrains. Using synthetic models, we applied machine learning to classify terrain shape and nearby features. 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 less orientations of faults.