基于循环神经网络的找矿模型构建与预测

在大数据和人工智能背景下,基于已有的传统地质找矿模型建立与应用基础,提出基于循环神经网络的找矿模型构建与预测方法,实现对地质数据的深入分析和理解。针对地质找矿模型构建与预测的需求,结合数据清洗理论,对传统地质找矿模型进行归纳与总结,建立地质找矿知识库,为深度学习算法提供训练数据。通过分类算法研究,综合对比结果的准确率与分类所用时间,最终选用RNN分类算法对找矿概念模型进行分类。在建立研究区找矿模型中,通过关键词与控矿要素完成模型匹配,利用模型计算对模型匹配结果进行数据分析,实现区域地质找矿模型的构建与矿产资源的预测评价和分析。以大水金矿为例,快速准确地实现了找矿模型的构建,有效地对矿产资源预测工作提供了指导,验证了该方法的可行性。

Construction and prediction of a prospecting model based on recurrent neural network

Under the background of big data and artificial intelligence and on the basis of the establishment and application basis of existing traditional geological prospecting model, this paper proposes a prospecting model construction and prediction method based on cyclic neural network, with the purpose of achieving in-depth analysis and understanding of geological data. According to the requirements for construction and prediction of geological prospecting model, the authors combined the data cleaning theory to systematically summarize and summarize the traditional geological prospecting model, thus establishing a geological prospecting knowledge base and providing training data for deep learning algorithms. The accuracy of the comparison results and the time used for classification were comprehensively analyzed. Finally, the RNN classification algorithm was selected to classify the conceptual model of prospecting. In the process of establishing the prospecting model of the study area, by using the key words and ore control elements to complete the model matching, the model was used to analyze the model matching results so as to realize the construction of the regional geological prospecting model and the prediction and analysis of the mineral resources. With the Dashui gold deposit as an example, the construction of the prospecting model was realized quickly and accurately, which effectively provides guidance for the prediction of mineral resources and verifies the feasibility of the method.