This paper presents a novel ML-based methodology for geothermal exploration
towards PFA applications. Our methodology is provided through our open-source
ML framework, GeoThermalCloud
\url{this https URL}. The GeoThermalCloud
uses a series of unsupervised, supervised, and physics-informed ML methods
available in SmartTensors AI platform \url{this https URL}.
Here, the presented analyses are performed using our unsupervised ML algorithm
called NMF$k$, which is available in the SmartTensors AI platform. Our ML
algorithm facilitates the discovery of new phenomena, hidden patterns, and
mechanisms that helps us to make informed decisions. Moreover, the
GeoThermalCloud enhances the collected PFA data and discovers signatures
representative of geothermal resources. Through GeoThermalCloud, we could
identify hidden patterns in the geothermal field data needed to discover blind
systems efficiently. Crucial geothermal signatures often overlooked in
traditional PFA are extracted using the GeoThermalCloud and analyzed by the
subject matter experts to provide ML-enhanced PFA, which is informative for
efficient exploration. We applied our ML methodology to various open-source
geothermal datasets within the U.S. (some of these are collected by past PFA
work). The results provide valuable insights into resource types within those
regions. This ML-enhanced workflow makes the GeoThermalCloud attractive for the
geothermal community to improve existing datasets and extract valuable
information often unnoticed during geothermal exploration.

本研究提出了一种基于机器学习的地热勘探方法，利用无监督、监督和物理学相关的算法，结合开源平台 GeoThermalCloud，提高资源发现的效率并发掘了隐藏的地热系统，发现通常在传统勘探中被忽略的关键地热指标，对于促进现有数据的提升以及提取有价值的信息具有重要意义。