  Accurate determination of the geothermal gradient is critical for assessing
the geothermal energy potential of a given region. Of particular interest is
the case of Colombia, a country with abundant geothermal resources. A history
of active oil and gas exploration and production has left drilled boreholes in
different geological settings, providing direct measurements of the geothermal
gradient. Unfortunately, large regions of the country where geothermal
resources might exist lack such measurements. Indirect geophysical measurements
are costly and difficult to perform at regional scales. Computational thermal
models could be constructed, but they require very detailed knowledge of the
underlying geology and uniform sampling of subsurface temperatures to be
well-constrained. We present an alternative approach that leverages recent
advances in supervised machine learning and available direct measurements to
predict the geothermal gradient in regions where only global-scale geophysical
datasets and course geological knowledge are available. We find that a Gradient
Boosted Regression Tree algorithm yields optimal predictions and extensively
validate the trained model. We show that predictions of our model are within
12% accuracy and that independent measurements performed by other authors agree
well with our model. Finnally, we present a geothermal gradient map for
Colombia that highlights regions where futher exploration and data collection
should be performed.
