Prediction of Prospecting Target Based on Selective Transfer Network

Huang, Yongjie; Feng, Qi; Zhang, Wanting; Zhang, Li; Gao, Le

doi:10.3390/min12091112

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…Deep learning has been actively applied in geological information metallogenic prediction and has achieved many innovative results [9][10][11][12][13][14]. Zuo and Carranza [15] used the SVM algorithm to predict the Nova Scotia gold deposit in western Canada, which confirmed that the SVM algorithm has obvious advantages in prediction accuracy and precision compared with the traditional weight of evidence method.…”

Section: Literature Review 21 Deep Learning For Metallogenic Predictionmentioning

confidence: 96%

“…The probability distribution after using softmax is, P = {P i } k i=1 , P i = [P 0 , P 1 ], where P 0 is the probability predicted to be "ore free" and P 1 is the probability predicted to be "ore present". We obtain the prediction results of each channel network through Formula (10) and determine the final prediction results through the vote of Formula (11). ŷi = argmax(P i )…”

Section: Fully Connected Layer Softmax and Votingmentioning

confidence: 99%

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Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Gao,

Wang,

Zhang

et al. 2023

Sustainability

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In recent years, the intelligent identification and prediction of ore deposits based on deep learning algorithm and image processing technology has gradually become one of the main 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) There are 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 geoimage data. The results show that the MFCA-Net module in the 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 in 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 the Qin-hang metallogenic belt. The experimental results show that the areas numbered 5, 9, 16, 28, 34, 41, 50, 72, 74, 75, 80, 97, 101, 124, and 130 have great metallogenic potential and this method would be a promising tool for metallogenic prediction. A large number of experimental results show that this method has obvious advantages over other state-of-the-art methods in the prediction of prospecting target areas, and the prediction effect in the samples with mines is greatly improved. The multi-scale feature fusion and attention mechanism MFAF in this paper can provide a new way of thinking for geologists in mineral exploration. The research of this paper also provides resource guarantees and technical support for the sustainable exploitation of mineral resources and the sustainable growth of society and economy.

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Section: Literature Review 21 Deep Learning For Metallogenic Predictionmentioning

confidence: 96%

Section: Fully Connected Layer Softmax and Votingmentioning

confidence: 99%

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Gao,

Wang,

Zhang

et al. 2023

Sustainability

Self Cite

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Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Gao,

Wang,

Zhang

et al. 2023

Preprint

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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.

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Research on Image Classification and Retrieval Using Deep Learning with Attention Mechanism on Diaspora Chinese Architectural Heritage in Jiangmen, China

Gao

Yang

et al. 2023

Buildings

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The study of the architectural heritage of the Chinese diaspora has an important role and significance in China’s historical and cultural background in the preservation of cultural data, the restoration of images, and in the analysis of human social and ideological conditions. The images from the architectural heritage of the Chinese diaspora usually include frescos, decorative patterns, chandelier base patterns, various architectural styles and other major types of architecture. Images of the architectural heritage of the Chinese diaspora in Jiangmen City, Guangdong Province, China are the research object of this study. A total of 5073 images of diaspora Chinese buildings in 64 villages and 16 towns were collected. In view of the fact that different types of image vary greatly in features while there are only small differences among the features of the same type of image, this study uses the depth learning method to design the Convolutional Neural Network Attention Retrieval Framework (CNNAR Framework). This approach can be divided into two stages. In the first stage, the transfer learning method is used to classify the image in question by transferring the trained parameters of the Paris500K datasets image source network to the target network for training, and thus the classified image is obtained. The advantage of this method is that it narrows the retrieval range of the target image. In the second stage, the fusion attention mechanism is used to extract the features of the images that have been classified, and the distance between similar images of the same type is reduced by loss of contrast. When we retrieve images, we can use the features extracted in the second stage to measure the similarities among them and return the retrieval results. The results show that the classification accuracy of the proposed method reaches 98.3% in the heritage image datasets of the JMI Chinese diaspora architectures. The mean Average Precision (mAP) of the proposed algorithm can reach 76.6%, which is better than several mainstream model algorithms. At the same time, the image results retrieved by the algorithm in this paper are very similar to those of the query image. In addition, the CNNAR retrieval framework proposed in this paper achieves accuracies of 71.8% and 72.5% on the public data sets Paris500K and Corel5K, respectively, which can be greatly generalized and can, therefore, also be effectively applied to other topics datasets. The JMI architectural heritage image database constructed in this study, which is rich in cultural connotations of diaspora Chinese homeland life, can provide strong and reliable data support for the follow-up study of the zeitgeist of the culture reflected in architecture and the integration of Chinese and Western aesthetics. At the same time, through the rapid identification, classification, and retrieval of precious architectural images stored in the database, similar target images can be retrieved reasonably and accurately; then, accurate techniques can be provided to restore old and damaged products of an architectural heritage.

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Prediction of Prospecting Target Based on Selective Transfer Network

Cited by 3 publications

References 43 publications

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Research on Image Classification and Retrieval Using Deep Learning with Attention Mechanism on Diaspora Chinese Architectural Heritage in Jiangmen, China

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