From Pixels to Deposits: Porphyry Mineralization With Multispectral Convolutional Neural Networks

Zidan, Usama and Desouky, Hamdy A. El and Gaber, Mohamed Medhat and Abdelsamea, Mohammed M. (2023) From Pixels to Deposits: Porphyry Mineralization With Multispectral Convolutional Neural Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16. pp. 9259-9271. ISSN 1939-1404

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Mineral exploration is essential to ensure a sustainable supply of raw materials for modern living and the transition to green. It implies a series of expensive operations that aim to identify areas with natural mineral concentration in the crust of the Earth. The rapid advances in artificial intelligence and remote sensing techniques can help in significantly reducing the cost of these operations. Here, we produce a robust intelligent mineral exploration model that can fingerprint potential locations of porphyry deposits, which are the world's most important source of copper and molybdenum and major source of gold, silver, and tin. We present a deep learning pipeline for assessing multispectral imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with the objective of identifying hydrothermal alterations. Our approach leverages a convolutional neural network (CNN) to analyze the high-resolution images, overcoming computational challenges through a patch-based strategy that involves an overlapping window for partitioning the images into fixed-size patches. Through the utilization of manually labeled patches for image classification and identification of hydrothermal alteration areas, our results demonstrate the remarkable ability of CNN to accurately detect hydrothermal alterations. The technique is adaptable for other ore deposit models and satellite imagery types, providing a revolution in satellite image interpretation and mineral exploration.

Item Type: Article
Identification Number:
1 October 2023Accepted
3 October 2023Published Online
Uncontrolled Keywords: Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), convolutional neural network (CNN), mineral exploration, multispectral image analysis
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Gemma Tonks
Date Deposited: 27 Nov 2023 16:07
Last Modified: 27 Nov 2023 16:07

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