Experimental Research on Big Data Mining and Intelligent Prediction of Prospecting Target Area - Application of Convolutional Neural Network Model

Metallogenic prediction needs to consider factors such as expert opinion, geological background, and metallogenic types under a comprehensive set of rules. However, due to the limitation of the biological conditions of human's actual computing ability, the biggest factor affecting the prospecting results of metallogenic prediction is the experience and knowledge of prospectors. With the advent of the era of big data, the metallogenic prediction can be regarded as mathematical calculation, that is, the metallogenic system is calculated according to specific rules, and the result is the metallogenic prospect expressed by probability. Relying on the computer's super-computing ability and machine learning methods and techniques, the computer can learn the characteristics of metallogenic prediction from big geological data, and realize the one-to-one verification of the correlation between different geological variables and ore bodies, so as to make predictions. In this paper, a case study of the Zhaojikou Pb-Zn ore deposit, Anhui province, is carried out to demonstrate how to use the convolutional neural network to learn the coupling relationship between the surficial distribution characteristics of Zn and the position of the ore body in the depth, and finally delineate the target area. After 450 trainings, a CNN model with 95% accuracy and 14% loss rate was obtained, and achieved intelligent delineation of three target areas. This neural network model may express the response of element distribution on the surface when the ore body was in deep underground, and can be used for ore mineral prospecting and delineation of target areas. © 2020, Science Press. All right reserved.