Published November 20, 2024
| Version v1.0.2
Software
Open
Machine learning-based spatio-temporal prospectivity modeling of porphyry systems in the New Guinea and Solomon Islands region
Creators
- 1. EarthByte Group, School of Geosciences, The University of Sydney, Sydney, Australia
- 2. John de Laeter Centre, Faculty of Science and Engineering, Curtin University, Perth, Australia
- 3. Lithodat Pty. Ltd., Melbourne, Australia
Description
Supplementary materials, including Python scripts, Jupyter Notebooks, and datasets used to create the mineral prospectivity maps presented in the paper: Farahbakhsh, E., Zahirovic, S., McInnes, B. I. A., Polanco, S., Kohlmann, F., Seton, M., M¨uller, R. D., Machine learning-based spatio-temporal prospectivity modelling of porphyry systems in the New Guinea and Solomon Islands region.
Files
e-farahbakhsh/STAMP_PNG-v1.0.1.zip
Files
(50.4 kB)
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Additional details
Related works
- Is supplement to
- Software: https://github.com/e-farahbakhsh/STAMP_PNG/tree/v1.0.1 (URL)
Software
- Repository URL
- https://github.com/EarthByte/STAMP_PNG
- Programming language
- Python
- Development Status
- Active