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Updated: Sep 10, 2025

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Improving electrical resistivity tomography interpretation with Gray Level Co-Occurrence Matrix textural attributes.

John McKnight1, Heather Bedle1, Sina Saneiyan2,3

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|August 22, 2025
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Summary

This study enhances electrical resistivity tomography (ERT) interpretation using Gray Level Co-Occurrence Matrix (GLCM) textural attributes. GLCM improves the delineation of subsurface clay boundaries and groundwater zones in complex geological settings.

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Area of Science:

  • Geophysics
  • Hydrogeology
  • Image Analysis

Background:

  • Electrical resistivity tomography (ERT) is crucial for subsurface imaging but interpretation is often ambiguous due to smooth boundaries from inversion.
  • Delineating clay boundaries is vital for groundwater exploration, but traditional ERT methods struggle with this in heterogeneous environments.

Purpose of the Study:

  • To apply Gray Level Co-Occurrence Matrix (GLCM) textural attributes to enhance ERT interpretation.
  • To improve the accuracy of subsurface characterization, particularly for groundwater resource assessment.

Main Methods:

  • Analysis of a 100 × 100 m 3D ERT survey.
  • Application of three GLCM textural attributes: Mean, Variance, and Entropy.
  • Integration of GLCM attributes with traditional ERT inversion.

Main Results:

  • GLCM Mean reduced inversion smoothing, improving clay boundary and lens morphology definition.
  • GLCM Variance identified a previously ambiguous surface-reaching recharge zone.
  • GLCM Entropy enhanced contrast between clay units and groundwater reservoirs, aiding aquifer and confining layer discrimination.

Conclusions:

  • GLCM textural analysis significantly enhances ERT interpretation in heterogeneous sedimentary environments.
  • This workflow provides valuable additional textural information from electrical resistivity data for improved subsurface characterization.
  • The method shows great potential for groundwater resource assessment and management.