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Automatic stochastic 3D clay fraction model from tTEM survey and borehole data
Alexis Neven1, Anders Vest Christiansen2, Philippe Renard3,4
1Centre of Hydrogeology and Geothermics, University of Neuchâtel, Neuchâtel, Switzerland. alexis.neven@unine.ch.
Scientific Reports
|October 12, 2022
Summary
This study introduces an automated method to integrate borehole and geophysical data for 3D subsurface modeling. The new workflow accurately predicts clay fraction and quantifies uncertainty, improving geological interpretations.
Area of Science:
- Geosciences
- Hydrogeology
- Geophysics
Background:
- Urbanized and agricultural areas in Central Europe extensively utilize Quaternary deposits for water, geothermal energy, and material extraction.
- The shallow underground in these regions exhibits high spatial variability and complexity due to intertwined geological layers.
- Existing methods for integrating geophysical and borehole data are time-consuming, lack realistic 3D interpolation, and often fail to represent data uncertainty.
Purpose of the Study:
- To develop a novel, automated methodology for combining borehole and geophysical data with uncertainty quantification for 3D subsurface modeling.
- To create a spatially varying translator function predicting clay fraction from resistivity, constrained by borehole data.
- To implement a 3D stochastic interpolation framework using Multiple Points Statistics and Gaussian Random Function for enhanced geological modeling.
Main Methods:
- Developed a spatially varying translator function to predict clay fraction from electrical resistivity.
- Utilized borehole descriptions as control points for inverting the translator function.
- Integrated the translator function with a 3D stochastic interpolation framework (Multiple Points Statistics and Gaussian Random Function).
- Applied the methodology to ground-based towed transient electromagnetic (tTEM) and borehole data in the upper Aare valley, Switzerland.
Main Results:
- Generated a high-resolution 3D model of clay fraction for the entire upper Aare valley.
- Successfully incorporated data and their uncertainties into the 3D geological model.
- Demonstrated the quality of predicted values and uncertainties through cross-validation using a dense dataset.
- Reduced user intervention compared to existing data integration workflows.
Conclusions:
- The proposed automated framework robustly combines diverse subsurface data, including uncertainty.
- The methodology provides realistic 3D geological models with high spatial resolution.
- This approach significantly enhances the efficiency and accuracy of subsurface characterization for various applications.
