Play fairway analysis (PFA) is a statistically based data integration and exploration tool originating in the oil and gas industry that has recently been applied to geothermal exploration. Here, we apply geothermal PFA to a ~40,000 km² area in the Argentine Puna of the South American Andes (average elevation >4,000 m aMSL) that has geothermal potential but no geothermal resource development to date. We develop a PF workflow customized to the local geological setting and availability of regional geoscience datasets. Evidence layers were grouped to model the spatial distribution of three primary geological attributes of a geothermal system: heat, permeability, and fluid. These layers included hot springs, geothermometry, age and distribution of volcanism, hydrothermal alteration, regional faults along with their kinematics and approximate age of most recent rupture, structural settings, and earthquakes. Spatial statistical methods were used to model and weight the distribution of these evidence layers, including distance buffers, point statistics, and weighted sum functions. Resulting favorability models were created using the veto (product) and voter (sum) methods, and both models indicate highest geothermal potential in Cerro Tuzgle/Aguas Calientes/Tocomar and west Coranzulí. Additional locations in the Puna study area may be prospective but are not resolved given the current data availability. Thus, further data collection to reduce the geological uncertainty is recommended, including acquisition of relatively low-cost datasets such as spring temperatures, fluid chemistry, detailed geological mapping, and structural characterization.

游戏航道分析（PFA）是基于统计的数据集成和勘探工具，起源于石油和天然气行业，最近已应用于地热勘探。在这里，我们将地热PFA应用于约40,000 km ²南美安第斯山脉的阿根廷普那地区（平均海拔> 4,000 m aMSL）具有地热潜力，但迄今为止尚未开发地热资源。我们开发了针对本地地质环境和区域地球科学数据集的可用性而定制的PF工作流。证据层被分组以模拟地热系统的三个主要地质属性：热量，渗透率和流体的空间分布。这些层包括温泉，地热法，火山作用的年龄和分布，热液蚀变，区域断层及其运动学以及最近破裂，构造背景和地震的大致年龄。使用空间统计方法对这些证据层的分布进行建模和加权，包括距离缓冲区，点统计和加权求和函数。使用否决（乘积）和表决器（求和）方法创建了最终的可喜度模型，这两个模型均显示了塞罗·图兹格/阿瓜斯·卡连特斯/ Tocomar和西科兰祖利的地热潜力最高。在Puna研究区中的其他位置可能是预期的，但鉴于当前的数据可用性，这些位置无法解决。因此，建议进一步收集数据以减少地质不确定性，包括获取相对低成本的数据集，例如弹簧温度，流体化学，详细的地质图和结构特征。在Puna研究区中的其他位置可能是预期的，但鉴于当前的数据可用性，这些位置无法解决。因此，建议进一步收集数据以减少地质不确定性，包括获取相对低成本的数据集，例如弹簧温度，流体化学，详细的地质图和结构特征。在Puna研究区中的其他位置可能是预期的，但鉴于当前的数据可用性，这些位置无法解决。因此，建议进一步收集数据以减少地质不确定性，包括获取相对低成本的数据集，例如弹簧温度，流体化学，详细的地质图和结构特征。