GeoThermalCloud.jl: Machine Learning framework for Geothermal Exploration
GeoThermalCloud.jl is a repository containing all the data and codes required to demonstrate applications of machine learning methods for geothermal exploration.
- site data
- simulation scripts
- jupyter notebooks
- intermediate results
- code outputs
- summary figures
- readme markdown files
GeoThermalCloud.jl showcases the machine learning analyses performed for the following geothermal sites:
- Brady: geothermal exploration of the Brady geothermal site, Nevada
- SWNM: geothermal exploration of the Southwest New Mexico (SWNM) region
- GreatBasin: geothermal exploration of the Great Basin region
Reports, research papers, and presentations summarizing these machine learning analyses are also available and will be posted soon.
GeoThermalCloud Machine Learning analyses are performed using Julia.
To install the most recent version of Julia, follow the instructions at https://julialang.org/downloads/
To install all required the modules, execute in the Julia REPL:
import Pkg Pkg.add("GeoThermalCloud")
GeoThermalCloud machine learning analyses can be executed as follows:
import Pkg Pkg.add("GeoThermalCloud") import GeoThermalCloud GeoThermalCloud.SWNM() # performs analyses of the Sounthwest New Mexico region GeoThermalCloud.GreatBasin() # performs analyses of the Great Basin region GeoThermalCloud.Brady() # performs analyses of the Brady site, Nevada
GeoThermalCloud machine learning analyses can be also executed as Jupyter notebooks as well
GeoThermalCloud.notebooks() # open Jupyter notebook to acccess all GeoThermalCloud notebooks GeoThermalCloud.SWNM(notebook=true) # opens Jupyter notebook for analyses of the Sounthwest New Mexico region GeoThermalCloud.GreatBasin(notebook=true) # opens Jupyter notebook for analyses of the Great Basin region GeoThermalCloud.Brady(notebook=true) # opens Jupyter notebook for analyses of the Brady site, Nevada
GeoThermalCloud analyses are performed using the SmartTensors machine learning framework.
SmartTensors provides tools for Unsupervised and Physics-Informed Machine Learning.
More information about SmartTensors can be found at smarttensors.github.io and tensors.lanl.gov.
SmartTensors includes a series of modules. Key modules are: