Gallery of examples

Below is a gallery of examples

• CS vs FS on synthetic conductive models
• Spatial data
  • Observations
• Comparison of inversion algorithm using the Cover Crop dataset
  • Observations
  • Sensitivity to \(\alpha\) (smoothing) parameter
  • Observations
• Time-lapse field application
• EMagPy Quickstart
  • Import EMagPy
  • Data import
  • Data visualization
  • Data inversion
• Inversion of an water-born survey over the river Leith
• Smoothing parameters and time-lapse inversion
  • Smoothing parameters
    • Vertical smoothing (alpha)
    • Lateral smoothing (beta)
    • Reference survey smoothing (gamma) or time-lapse inversion
    • Observations
  • Inverting change in ECa
• Effect of noise and height on the inversion
• Different forward models
• 3D visualization
• Multi-frequency inversion with EMagPy
• Benchmarking the different inversion methods
• Sensitivity, Gauss-Newton and DOI
  • Sensitivity
  • Gauss-Newton approach
• Depth of investigation
• Calibrated and not calibrated ERT with Boxford dataset
• GF Instruments correction
  • What is the purpose of the calibration?
  • How the GF calibration works?
  • Impact on the inversion
    • Observations:
  • Conclusions

Fig. 8 CS vs FS on synthetic conductive models

Fig. 9 Spatial data

Fig. 10 Comparison of inversion algorithm using the Cover Crop dataset

Fig. 11 Time-lapse field application

Fig. 12 EMagPy Quickstart

Fig. 13 Inversion of an water-born survey over the river Leith

Fig. 14 Smoothing parameters and time-lapse inversion

Fig. 15 Effect of noise and height on the inversion

Fig. 16 Different forward models

Fig. 17 3D visualization

Fig. 18 Multi-frequency inversion with EMagPy

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Fig. 19 Benchmarking the different inversion methods

Fig. 20 Sensitivity, Gauss-Newton and DOI

Fig. 21 Calibrated and not calibrated ERT with Boxford dataset

Fig. 22 GF Instruments correction