ISSN 1006-3021 CN11-3474/P
Published bimonthly started in 1979
关键词:3D predictive modeling  Dayingezhuang gold deposit  convolutional neural networks  feature extraction
基金项目:国家重点研发计划“深地资源勘查开采”重点专项课题(编号: 2017YFC0601503);国家自然科学基金项目(编号: 41972309; 41772349)
邓浩 中南大学地球科学与信息物理学院
郑扬 中南大学地球科学与信息物理学院  
陈进 中南大学地球科学与信息物理学院
魏运凤 中南大学地球科学与信息物理学院  
毛先成 中南大学地球科学与信息物理学院
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Deep Learning-based 3D Prediction Model for the Dayingezhuang Gold Deposit, Shandong Province
      In the three-dimensional (3D) prediction of buried orebodies, the performance of the prediction model depends majorly on the indication of predictor variables to the mineralized enrichment site. The establishment of predictor variables depends on the reliability of metallogenic conceptual model and the effectiveness of ore-prospecting information extraction, which, however is easily hindered by geological experience and ore-forming information extraction method. With the 3D prediction of buried orebodies in the Danyingezhuang gold deposit in Shandong Province as an example, this paper propose a prediction modeling method based on deep learning, with the purpose of using deep networks to obtain significant predictor variables and improve the accuracy of 3D prediction driven by geological data. In this method, the 3D geological model and its shape features are transformed into two-dimensional images suitable for the convolutional networks. The convolutional neural network is adopted to realize the automatic extraction of ore-prospecting variables, and the quantitative correlation between the 3D geological model and the mineralized enrichment areas is thus constructed. By using this method, the 3D prediction model in the Danyingezhuang gold deposit was established. The comparison between the proposed mothod and several traditional modeling methods shows that the prediction model based on deep learning greatly improves the prediction accuracy.
DENG Hao,ZHENG Yang,CHEN Jin,WEI Yun-feng,MAO Xian-cheng.2020.Deep Learning-based 3D Prediction Model for the Dayingezhuang Gold Deposit, Shandong Province[J].Acta Geoscientica Sinica,41(2):157-165.
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