At present, most prediction methods of prospecting prediction rely on manual sampling and experts' knowledge and experience. However, these methods face great challenges in the real world with small areas and less numbers of mines. In order to meet this challenge, this paper proposed a novel depth prospecting prediction framework: multi-scale feature interaction framework. Firstly, this paper defined two networks, the multi-scale feature mapping net and the multi-scale feature classification net. On this basis, it captured the features of different geochemical elements in the multi-scale feature mapping net by dilated convolution, and used the multi-scale feature classification net to process these features. Secondly, it used meta network to generate convolutional weights for multi-scale classification networks. Finally, it used self-distillation to exploit implicit knowledge in multi-scale classification networks for prediction. The whole model adopted end-to-end training mode. A large number of experimental results show that multi-scale feature interaction framework is significantly competitive with the most advanced methods.

[2022-01-24] Accepted Paper /doc/2021.10.0625/v1

[2022-06-05] Printed Article /doc/2021.10.0625/v2