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Tying geophysics to hydrogeology: a learning machine approach to characterise heterogeneous granular aquifers

A learning machine approach is proposed to define site-specific hydro-geophysical relationships in order to predict granular aquifer hydraulic properties from geophysical measurements. The learning machine is trained on a representative data set of hydraulic and geophysical measurements.
The main algorithms used for training are semisupervised fuzzy clustering and relevant vector machines (RVM) for classification and regression. This approach, which extends the capabilities of geophysical methods, represents an efficient alternative to conventional granular aquifer characterization
mainly based on hydraulic methods.

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