The very shallow geothermal potential (vSGP) is increasingly recognized as a viable resource for providing clean thermal energy in urban and rural areas. This is primarily due to its reliability, low-cost installation, easy maintenance, and little constraints regarding ground-related laws and policies. We propose a methodology to extract the theoretical vSGP (installed in the uppermost 10 m of the ground, and mostly at depths of 1–2 m) at the national scale for Switzerland, based on a combination of Geographic Information Systems, traditional modelling, and machine learning (ML). The theoretical vSGP is based on the estimation of three thermal characteristics of the ground that impact significantly the geothermal potential, namely the monthly temperature at various depths in the surface layer, the thermal conductivity, and the thermal diffusivity. Each of the three variables is estimated separately, to a depth of 1 m below the surface, using the following general strategy: (1) collect significant data related to the variable, (2) if not existing, extract values for the variable at available locations with the help of traditional models and part of the data as input for these models, (3) train a ML model (with the Random Forests algorithm) using the extracted variable values as examples (training output labels) and related information contained in the data as features (training input samples), (4) use the trained ML model to estimate the variable in unknown locations, (5) estimate the uncertainty attached to the estimations. The methodology estimates values for (200 × 200) (m2) pixels forming a grid over Switzerland. The strategy, however, can be generalized to any country with significant data (topographic, weather, and surface layer/soil data) available. The results indicate a very non-negligible potential for very shallow geothermal systems in Switzerland.

中文翻译：

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一种用于绘制非常浅的理论地热势的机器学习方法

极浅的地热潜力（vSGP）被日益认为是在城市和农村地区提供清洁热能的可行资源。这主要是由于其可靠性，低成本安装，易于维护以及对与地面相关的法律和政策的限制很少。我们建议结合瑞士的地理信息系统，传统建模方法和方法，在瑞士全国范围内提取理论vSGP（安装在地面的最高10 m，且大多数位于1-2 m的深度）。机器学习（ML）。理论上的vSGP基于对地热势有显着影响的三个地热特征的估算，即地表层各个深度处的月度温度，热导率和热扩散率。使用以下一般策略分别对三个变量中的每个变量进行估计，深度为地下1 m：（1）收集与变量相关的重要数据，（2）如果不存在，则在可用位置提取变量的值借助传统模型和部分数据作为这些模型的输入，（3）使用提取的变量值作为示例（训练输出标签）以及包含在模型中的相关信息来训练ML模型（使用随机森林算法）。数据作为特征（训练输入样本），（4）使用训练后的ML模型来估计未知位置的变量，（5）估计附加到估计中的不确定性。该方法估计在瑞士上形成网格的（200×200）（m2）像素的值。但是，该策略 可以推广到具有大量可用数据（地形，天气和表层/土壤数据）的任何国家。结果表明，瑞士极浅层地热系统的潜力不可忽略。