Evaluating unsupervised machine learning techniques for geological mapping with airborne geophysical data: case study of Shahr-e-Babak, Iran
Crossref DOI link: https://doi.org/10.1007/s12145-025-02006-5
Published Online: 2025-12-10
Published Print: 2026-01
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Jahantigh, Moslem
Ramazi, Hamidreza
Text and Data Mining valid from 2025-12-10
Version of Record valid from 2025-12-10
Article History
Received: 24 May 2025
Accepted: 22 August 2025
First Online: 10 December 2025
Declarations
:
: The authors declare no competing interests.