基于大数据智能的找矿模型构建与预测

摘要

当前地质科学数据呈现出科学大数据的特点,依靠传统人工检索和处理地质大数据具有很大的局限性,难以满足当前地质科学高速发展的需求.针对找矿地质模型建立与预测需求,本文利用大数据发现方法实现了地质找矿专题数据的自动采集;利用机器学习方法对地质专题数据进行深层次的挖掘和提取,研究了基于大数据智能的找矿模型预测方法.在已有地质成矿理论的基础上,建立了统一的多数据源找矿地质模型库,使用朴素贝叶斯分类算法对找矿概念模型库中数据进行分类研究,通过计算模型中控矿要素的使用率和重要性来建立起全面客观的找矿地质模型,最终实现找矿模型预测.%Geological science data present the characteristic of big data.Traditional manual retrieval and processing geological data has great limitations.It is difficult to meet the high-speed development requirement of the current geological science.Aiming at the establishment and prediction of prospecting geological model,this paper makes use of the big data discovery method to realize the automatic collection of geological prospecting thematic data.By using the machine learning method,the geological thematic data is mining deeply,and the prediction method of prospecting model based on big data intelligence is researched.On the basis of the existing geological metallogenic theory,a unified geological prospecting model library of multi-source data is established.Naive bayesian classification algorithm is used for prospecting concept model library classify data.By calculating model control utilization rate of mineral elements and importance,the comprehensive and objective prospecting geological model is establish to realize the prediction of prospecting model.