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This paper presents a novel ML-based methodology for geothermal explorationtowards PFA applications. Our methodology is provided through our open-sourceML framework, GeoThermalCloud\url{https://github.com/SmartTensors/GeoThermalCloud.jl}. The GeoThermalClouduses a series of unsupervised, supervised, and physics-informed ML methodsavailable in SmartTensors AI platform \url{https://github.com/SmartTensors}.Here, the presented analyses are performed using our unsupervised ML algorithmcalled NMF$k$, which is available in the SmartTensors AI platform. Our MLalgorithm facilitates the discovery of new phenomena, hidden patterns, andmechanisms that helps us to make informed decisions. Moreover, theGeoThermalCloud enhances the collected PFA data and discovers signaturesrepresentative of geothermal resources. Through GeoThermalCloud, we couldidentify hidden patterns in the geothermal field data needed to discover blindsystems efficiently. Crucial geothermal signatures often overlooked intraditional PFA are extracted using the GeoThermalCloud and analyzed by thesubject matter experts to provide ML-enhanced PFA, which is informative forefficient exploration. We applied our ML methodology to various open-sourcegeothermal datasets within the U.S. (some of these are collected by past PFAwork). The results provide valuable insights into resource types within thoseregions. This ML-enhanced workflow makes the GeoThermalCloud attractive for thegeothermal community to improve existing datasets and extract valuableinformation often unnoticed during geothermal exploration.

GeoThermalCloud: Machine Learning for Geothermal Resource Exploration