Convolutional neural networks for mineral prospecting through alteration mapping with remote sensing data

TL;DR

This study demonstrates that convolutional neural networks, trained with ground truth data and combined with principal component analysis, enhance the accuracy of remote sensing-based mineral alteration mapping compared to traditional methods.

Contribution

It introduces a CNN-based approach for mineral alteration mapping using Landsat and ASTER data, outperforming traditional machine learning models in geological feature detection.

Findings

CNNs outperform traditional models in mapping alteration zones.

Landsat 9 provides better iron oxide mapping than Landsat 8.

ASTER data yields the most accurate alteration maps.

Abstract

Traditional geological mapping, based on field observations and rock sample analysis, is inefficient for continuous spatial mapping of features like alteration zones. Deep learning models, such as convolutional neural networks (CNNs), have revolutionised remote sensing data analysis by automatically extracting features for classification and regression tasks. CNNs can detect specific mineralogical changes linked to mineralisation by identifying subtle features in remote sensing data. This study uses CNNs with Landsat 8, Landsat 9, and ASTER data to map alteration zones north of Broken Hill, New South Wales, Australia. The model is trained using ground truth data and an automated approach with selective principal component analysis (PCA). We compare CNNs with traditional machine learning models, including k-nearest neighbours, support vector machines, and multilayer perceptron. Results show that ground truth-based training yields more reliable maps, with CNNs slightly outperforming conventional models in capturing spatial patterns. Landsat 9 outperforms Landsat 8 in mapping iron oxide areas using ground truth-trained CNNs, while ASTER data provides the most accurate argillic and propylitic alteration maps. This highlights CNNs' effectiveness in improving geological mapping precision, especially for identifying subtle mineralisation-related alterations.