DCGAN-Based Feature Augmentation: A Novel Approach for Efficient Mineralization Prediction Through Data Generation

This study aims to improve the efficiency of mineral exploration by introducing a novel application of Deep Convolutional Generative Adversarial Networks (DCGANs) to augment geological evidence layers. By training a DCGAN model with existing geological, geochemical, and remote sensing data, we have...

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Veröffentlicht in:	Minerals (Basel) 2025-01, Vol.15 (1), p.71
Hauptverfasser:	Qaderi, Soran, Maghsoudi, Abbas, Pour, Amin Beiranvand, Rajabi, Abdorrahman, Yousefi, Mahyar
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Decision making Fault lines Generative adversarial networks Geological mapping Geology Lithology Mineral deposits Mineral exploration Mineralization Minerals Neural networks Remote sensing Sediments Stone
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