Smooth, differentiable mapping in the multidimensional feature space providing more accurate subsurface property and variability estimates
R. Ghose (TU Delft - Civil Engineering & Geosciences)
E. Revelo Obando (TU Delft - Civil Engineering & Geosciences)
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Abstract
Geophysical parameters are used to derive subsurface properties and their spatial variabilities. Accuracy of this derivation based on correlation depends on accounting for the multivariate nature of the target properties. In this research, we investigate the multidimensional feature space linking seismic shear-wave velocity (VS) to geotechnical cone tip resistance (qc) and sleeve friction (fs). On a regional training dataset, we find that a smooth differential contributes to high accuracy of mapping of the points in the multidimensional feature space, even when a limited number of features are used. This leads to more accurate prediction of the properties than with piecewise constant regressions.