aceMetaheuristic-optimized machine learning framework for remote sensing-based alteration mapping of porphyry copper systems
Remote sensing-based hydrothermal alteration mapping is a pivotal technique in critical mineral exploration, particularly for identifying porphyry copper deposits (PCDs). However, conventional multispectral approaches are often constrained by linear assumptions, spectral mixing, and suboptimal parameter selection, limiting their ability to resolve complex alteration assemblages. This study presents a metaheuristic-optimized machine learning framework that integrates Boosted Trees (BT) and Quadratic Support Vecto…
Integrating Shuffled Frog Leaping Algorithm optimization with Boosted Trees and QSVM classifiers improved discrimination of argillic, phyllic, propylitic and iron oxide alteration zones from ASTER and Sentinel-2 data, raising AUC and achieving strong field validation.
Evidence
- Peer-reviewedScientific Reports2026-06-07
How should this claim be treated?
Truvace Impact Record TRV-2026-0167, v1: “Metaheuristic-optimized machine learning framework for remote sensing-based alteration mapping of porphyry copper systems.” Truvace, 2026-07-13. /record/TRV-2026-0167 (accessed at citation time). sha256 105717856ad7f5f1…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0167 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.