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

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.