Gao, L.; Wang, K.; Zhang, X.; Wang, C. Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning. Sustainability2023, 15, 10269.
Gao, L.; Wang, K.; Zhang, X.; Wang, C. Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning. Sustainability 2023, 15, 10269.
Gao, L.; Wang, K.; Zhang, X.; Wang, C. Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning. Sustainability2023, 15, 10269.
Gao, L.; Wang, K.; Zhang, X.; Wang, C. Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning. Sustainability 2023, 15, 10269.
Abstract
In recent years, intelligent identification and prediction of ore deposits based on deep learning algorithm and image processing technology has gradually become one of the research frontiers in the field of geological and metallogenic prediction. However, this method also has many problems that need to be solved. For example, (1) very few trainable image samples containing mineral point labels; (2) The geological image features are small and irregular, and the image similarity is high; (3) It is difficult to calculate the influence of different geological prospecting factors on ore mineralization. Based on this, this paper constructs a deep learning network model Multiscale Feature Attention Framework (MFAF) based on geo-image data. The results show that MFCA-Net module in MFAF model can solve the problem of scarce mine label images to a certain extent. In addition, the channel attention mechanism SE-Net module can quantify the difference of influence of different source factors on mineralization. The prediction map is obtained by applying the MFAF model in the study of deposit identification and prediction in the research area of the southern section of Qin-hang metallogenic Belt. In the forecast map, many regions have high metallogenic potential. Through observation, it is found that the above predicted area covers 100% of the known ore deposits at present, which has a good prediction effect. Later, we can find out the specific reasons through field verification. The multi-scale feature fusion and attention mechanism MFA Framework in this paper can provide a new way of thinking for geologists in mineral exploration. The research of this paper also provides resource guarantee and technical support for the sustainable exploitation of mineral resources and the sustainable growth of society and economy.
Keywords
Artificial Intelligence; Sustainable development; Deep Learning; Mineral Deposit Prediction; Multi-scale Features; Attention Mechanism
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
