2:35 PM - 2:55 PM
[3108-13-05] Development of Mining Exploration through AI and Georeferenced Hyperspectral/Multispectral Datasets, Pilot Along Nickel Based Zone
Chairperson：玉村 修司（幌延地圏環境研究所）
Keywords:Nickel based zone, Georeferenced , Machine learning, Hyperspectral/Multispectral, Exploration
The integration of Artificial Intelligence (AI) with georeferenced hyperspectral and multispectral imagery datasets represents a transformative approach in the field of mining exploration. This approach uses advanced machine learning algorithms to improve mineral deposit identification and analysis, enhancing exploration efficiency and accuracy. These imaging techniques capture detailed characteristics of features along Nickel Based Zone. Georeferencing images provides spatial accuracy and links all related datasets to geographic coordinates, ensuring precise and efficient resource use to facilitate potential mapping of mineral resources.
AI, Machine Learning (ML), handles extensive hyperspectral and multispectral data from various sensors using Neural Network Training (NNT) techniques such as Autoencoders, Principal Component Analysis (PCA), and Convolutional Neural Networks (CNNs). Additionally, ML continuously adapts and improves by learning from new data. These methods learn efficient data representations, capture essential features, reduce dataset dimensionality, enhance feature extraction and spectral signature generation. The synergy of AI and georeferenced hyperspectral/multispectral datasets automates tasks, saving time and effort, while handling diverse data types and large capacities, thereby reducing exploration costs through real time automated analysis and processing. Additionally, green exploration trends are supported, minimizing environmental impact.
In conclusion, integrating AI with georeferenced hyperspectral/multispectral datasets advances significantly mining exploration. Improved efficiency, accuracy, and sustainability in mineral exploration lead to better resource management practices. As technology evolves, AI`s role in mining will expand to offer new opportunities for the discovery and utilization of mineral resources.
AI, Machine Learning (ML), handles extensive hyperspectral and multispectral data from various sensors using Neural Network Training (NNT) techniques such as Autoencoders, Principal Component Analysis (PCA), and Convolutional Neural Networks (CNNs). Additionally, ML continuously adapts and improves by learning from new data. These methods learn efficient data representations, capture essential features, reduce dataset dimensionality, enhance feature extraction and spectral signature generation. The synergy of AI and georeferenced hyperspectral/multispectral datasets automates tasks, saving time and effort, while handling diverse data types and large capacities, thereby reducing exploration costs through real time automated analysis and processing. Additionally, green exploration trends are supported, minimizing environmental impact.
In conclusion, integrating AI with georeferenced hyperspectral/multispectral datasets advances significantly mining exploration. Improved efficiency, accuracy, and sustainability in mineral exploration lead to better resource management practices. As technology evolves, AI`s role in mining will expand to offer new opportunities for the discovery and utilization of mineral resources.
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