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.

翻译：精确确定地温梯度对于评估某一地区的地热能源潜力至关重要。尤为关注的是哥伦比亚，这个拥有丰富地热资源的国家。活跃的油气勘探与生产历史在不同地质环境中留下了钻探井孔，提供了地温梯度的直接测量数据。然而，该国可能存在地热资源的大片区域缺乏此类测量。地球物理间接测量成本高昂，且难以在区域尺度上进行。可构建计算热模型，但这需要非常详细的底层地质知识以及均匀的地下温度采样才能得到良好约束。我们提出一种替代方法，利用监督式机器学习的最新进展及现有的直接测量数据，在仅有全球尺度地球物理数据集和粗略地质知识可用的区域预测地温梯度。我们发现梯度提升回归树算法能产生最优预测，并对训练后的模型进行了广泛验证。我们表明，模型预测的精度在12%以内，其他研究者进行的独立测量与我们的模型吻合良好。最后，我们绘制了哥伦比亚地温梯度图，突出了应进一步勘探和收集数据的区域。