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Area of Science:

  • Geology
  • Image Analysis
  • Artificial Intelligence

Background:

  • Fracture detection in geological samples is crucial for understanding rock mechanics and resource exploration.
  • Traditional methods struggle with low contrast, noise, and artifacts common in X-ray computed tomography (CT) data.
  • Heterogeneous geological materials, like marly limestone, present unique challenges for fracture characterization.

Purpose of the Study:

  • To develop and validate a robust methodology for detecting and characterizing fractures in geological samples using X-ray CT.
  • To improve the precision and reliability of fracture network identification and quantification.
  • To address uncertainties arising from CT system limitations and geological sample characteristics.

Main Methods:

  • Integration of convolution-based image processing with neural network-based segmentation for fracture detection.
  • Application of preprocessing techniques including Gaussian and median filtering, multi-angle scanning, and intensity normalization.
  • Validation on a marly limestone sample from the Maiolica Formation, Italy, known for complex fracture patterns.

Main Results:

  • The proposed methodology achieved high precision in identifying complex fracture networks, outperforming traditional convolution-based methods.
  • Neural network segmentation effectively distinguished fractures filled with calcite or clays from the host rock.
  • The workflow significantly improved the reliability and accuracy of fracture quantification in challenging CT data.

Conclusions:

  • The combined CT and neural network approach offers a powerful and reproducible framework for analyzing discontinuities in complex geological materials.
  • This method enhances the understanding of fracture systems in geologically significant formations.
  • The study provides a reliable tool for accurate fracture quantification, crucial for various geoscience applications.