Prediction of Prospecting Target Based on Selective Transfer Network

Abstract

In recent years, with the integration and development of artificial intelligence technology and geology, traditional geological prospecting has begun to change to intelligent prospecting. Intelligent prospecting mainly uses machine learning technology to predict the prospecting target area by mining the correlation between geological variables and metallogenic characteristics, which usually requires a large amount of data for training. However, there are some problems in the actual research, such as fewer geological sample data and irregular mining features, which affect the accuracy and reliability of intelligent prospecting prediction. Taking the Pangxidong study area in Guangdong Province as an example, this paper proposes a deep learning framework (SKT) for prospecting target prediction based on selective knowledge transfer and carries out intelligent prospecting target prediction research based on geochemical data in Pangxidong. The irregular features of different scales in the mining area are captured by dilation convolution, and the weight parameters of the source network are selectively transferred to different target networks for training, so as to increase the generalization performance of the model. 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, which can effectively alleviate the problems of a small number of geological samples and irregular features of mining areas in prospecting prediction.

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

“…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.…”

“…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 )…”