Abstract
Mineral resources are indispensable in the development of human society and are the foundation of national economic development. As the prospecting target shifts from outcrop ore to concealed ore, from shallow to deep, the difficulty of prospecting becomes more and more difficult. Therefore, the prediction of mineralization prospects is of great significance. This paper is aimed at completing the prediction of mineralization prospects by constructing geological semantic models and using mobile computer learning to improve the accuracy of prediction of mineralization prospects and expanding the application of semantic mobile computing. We use five different semantic relations to build a semantic knowledge library, realize semantic retrieval, complete information extraction of geological text data, and study mineral profiles. Through the distributed database of mobile computing, the association rules and random forest algorithm are used to describe the characteristics of minerals and the ore-controlling elements, find the association rules, and finally combine the geological and mineral data of the area and use the random forest algorithm to realize the prospect of mineralization district forecast. The geological semantic model constructed in the article uses the knowledge library for associative search to achieve an accuracy rate of 87.9% and a recall rate of 96.5%. The retrieval effect is much higher than that of traditional keyword retrieval methods. The maximum value of the posterior result of the mineralization prospect is 0.9027, the average value is 0.0421, and the standard deviation is 0.1069. The picture is brighter, and the probability of mineralization is higher.
Highlights
Contemporary mineral resource evaluation is the modeling and evaluation process of complex high-dimensional nonlinear systems
The results show that the machine learning agent obtained using the proposed iterative learning process provides a fairly accurate real model agent and greatly reduces the calculation time required for large-scale parameter space exploration and calibration
This article is mainly based on the study of geological semantic model and mobile computing machine learning to predict the mineralization prospects, constructs the geological semantic model, uses the rule algorithm and random forest algorithm in machine learning, and completes the collection and preprocessing of geological information which achieved a high recall rate and precision rate
Summary
Contemporary mineral resource evaluation is the modeling and evaluation process of complex high-dimensional nonlinear systems. Mineralization prediction is based on the guidance of scientific prediction theory, applying geological and mineralization theories and mathematical statistics to comprehensively study geology, geophysics, geochemistry, and remote sensing. We analyze mineralization geological conditions, summarize mineralization rules, establish comprehensive information mineralization prediction model, delineate and evaluate mineralization prospects, provide scientific basis for regional prospecting work deployment, and overall plan mineral resources development. With the rapid development of computer technology, the digitization of geological information has become more and more popular, prompting the application of mathematical methods, mobile computing, and machine learning in mineralization prediction more and more. We applying the learning method of the geological semantic model deterioration mobile computing machine to the prediction of mineral resources, aim to analyze how to efficiently use geological and mineral information under complex geological conditions, and realize the automation, intelligence, and accuracy of the prediction process of mineralization prospects
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