Calibration, Conversion, and Quantitative Multi-Layer Inversion of Multi-Coil Rigid-Boom Electromagnetic Induction...

Area of Science:

• Geophysics
• Environmental Science
• Electrical Engineering

Background:

• Multi-coil electromagnetic induction (EMI) systems measure subsurface apparent electrical conductivity (σa).
• Traditional EMI data processing can be unreliable due to above-ground influences and signal alterations.
• Accurate electrical conductivity models are crucial for subsurface characterization and monitoring.

Purpose of the Study:

• To develop an improved data processing workflow for multi-coil EMI data.
• To enhance the calibration, conversion, and inversion of EMI measurements for greater accuracy.
• To improve the reliability of subsurface electrical conductivity models derived from EMI data.

Main Methods:

• Comparison of three direct current resistivity techniques (Dipole-Dipole, Schlumberger, vertical electrical soundings) for EMI data calibration.
• Implementation of a non-linear exact EMI conversion method for magnetic field to σa conversion.
• Development of a complete processing workflow integrating calibration, conversion, and inversion.

Main Results:

• All three direct current resistivity methods provided robust calibration results for σa.
• Dipole-Dipole-based calibration demonstrated stability across different soil types.
• The proposed workflow yields accurate, quantitative EMI data and reliable intrinsic electrical conductivity estimates.

Conclusions:

• The enhanced EMI data processing workflow significantly improves the accuracy and reliability of subsurface electrical conductivity models.
• This advancement facilitates better integration of EMI data with other geophysical methods like remote sensing.
• The improved methodology supports more effective subsurface monitoring applications.