Abstract Machine learning algorithms have been applied widely in the fields of natural
science, social science and engineering. It can be expected that machine learning …

Supervised and unsupervised learning methods are widely used to classify and cluster
multivariate geochemical data. Supervised learning methods incorporate training functions …

In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to
model subsurface gold mineralization by using a combination of the surface soil …

Geochemical anomaly detection is an important issue in mineral exploration. The availability
of a training dataset consisting of labeled geochemical samples of background and anomaly …

This study combines geochemical anomaly separation with geostatistical approaches and
compositional data analysis. To have a reasonable model for abnormal areas, suggesting …

This contribution conceptualizes a blind source separation (BSS) model to recover sources
of geochemical signals such that multi-depth ore-related enrichment patterns in complex …

The Kivi area in the East Azerbaijan Province of Iran is one of the country's highest-potential
regions for metal element exploration. The primary goal herein was to process the data …

Hyperspectral images have been extensively employed to map alterations related to various
ore deposits, particularly those associated with porphyry copper deposits. The present study …

Abstract The Miocene Kuh-Panj porphyry copper deposit, with reserve estimated at about
100 Mt. at 0.21% Cu, is located 120 km SW of Kerman in the southern part of the Urumieh …

Analyzing geochemical data from stream sediment samples is one of the most proactive
tools in the geochemical modeling of ore mineralization and mineral exploration. The main …