Integrating multi-source geoscience data with RF-weighted conditional variational autoencoders (CVAE) for porphyry copper prospectivity mapping: an application to the Ardestan district, Central Iran
- DOI
- 10.1038/s41598-026-61085-6
- Published
- 2026-07-21
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-61085-6,
title = {Integrating multi-source geoscience data with RF-weighted conditional variational autoencoders (CVAE) for porphyry copper prospectivity mapping: an application to the Ardestan district, Central Iran},
author = {Samane Esmaelzade Kalkhoran and Seyyed Saeed Ghannadpour},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-61085-6},
url = {https://doi.org/10.1038/s41598-026-61085-6}
}RIS
TY - JOUR TI - Integrating multi-source geoscience data with RF-weighted conditional variational autoencoders (CVAE) for porphyry copper prospectivity mapping: an application to the Ardestan district, Central Iran AU - Samane Esmaelzade Kalkhoran AU - Seyyed Saeed Ghannadpour PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-61085-6 UR - https://doi.org/10.1038/s41598-026-61085-6 ER -
APA
Kalkhoran, S. E., & Ghannadpour, S. S. (2026). Integrating multi-source geoscience data with RF-weighted conditional variational autoencoders (CVAE) for porphyry copper prospectivity mapping: an application to the Ardestan district, Central Iran. Scientific Reports. https://doi.org/10.1038/s41598-026-61085-6
Source records
- crossref · retrieved 2026-09-26T06:32:28.950Z