Polymetallic mineralization prospectivity modelling using multi-geospatial data in logistic regression: The Diapiric Zone, Northeastern Algeria

Abstract

Prospecting and exploring minerals present major challenges in tectonically complex regions for sustainable development as in Northeastern Algeria. This area is promising for its mineral potential, especially the metallogenic province "The Diapiric Zone". This study concerns mapping and predicting potential polymetallic mineralization locations by integration of remote sensing, gravity, and magnetic datasets. Several enhancement and processing methods have been applied on Landsat8_OLI and ASTER_1T remote sensed data to reduce uncertainty for achieving the best detection of hydrothermal alteration zones and lithological mapping. Furthermore, the Centre for Exploration Targeting grid analysis technique, the contact occurrence density and entropy orientation tools were employed on ground-gravity and aeromagnetic data to understand and visualize the pathways for hydrothermal fluids circulation of mineral deposits. The polymetallic mineralization prospective areas were produced using a logistic regression model on the resulting multifactor. High zones of lead-zinc cover most the area that has been confirmed by field investigation.