Random forest (RF) classification was applied to 37 predictor maps (vectors to
mineralization) producing a Mineral Prospectivity Map (MPM) for volcanogenic massive …

Significant and readily accessible orogenic gold deposits have been previously recognized,
exploited, and progressively depleted. Innovative approaches are required to discover new …

Abstract Mineral Prospectivity Map** has been applied to define exploration targets for
orogenic gold mineralization in the world-class Malartic-Val-d'Or area (Quebec) of the Abitibi …

This study investigates the challenges and opportunities presented by integrating genetic
algorithm (GA) with artificial intelligence-based mineral prospectivity map** (AI-MPM) for …

Mineralization temperature determination is fundamental to economic geology research, yet
quantifying it across mineralization remains a challenge. Sphalerite is ubiquitous in various …

Abstract Machine learning improves geochemistry discriminant diagrams in classifying
mineral deposit genetic types. However, the increasingly recognized “black box” property of …

Determining the spatial thickness (z) of in situ and reworked fallout pyroclastic deposits
plays a key role in volcanological studies and in shedding light on geomorphological and …

Global demand for critical raw materials, including phosphorus (P) and rare earth elements
(REEs), is on the rise. The south part of Norway, with a particular focus on the Southern Oslo …

Mineral prospectivity maps were produced for gold in two greenstone belts in the Superior
geological province in Ontario, Canada, as part of the Metal Earth Project in the Laurentian …

Our research focuses on examining two clustering methods, namely Density-Based Spatial
Clustering of Applications with Noise (DBSCAN) and fuzzy c-means (FCM) algorithms, to …