HIGHLIGHTS
SUMMARY
The most widely used deep learning algorithms in the field of mineral prediction mainly include convolutional neural_networks (CNNs), recurrent neural_networks (RNNs), stack automatic coding (stack denoising automatic coding and stacked sparse autoencoder), deep networks with a restricted Boltzmann machine as the core (deep belief network and deep Boltzmann machine) (DBN)), and the fully convolutional neural_network (FCN). While guaranteeing the similarity of the metallogenic background in the study area, the area with the most abundant data is taken as a pre-training area, and the convolution kernel is transferred to the target study area to . . .
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