| 基于AlexNet网络的二维找矿预测——以松桃—花垣地区沉积型锰矿为例 |
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| 引用本文： | 李诗,陈建平,向杰,张志平,张烨. 基于AlexNet网络的二维找矿预测——以松桃—花垣地区沉积型锰矿为例[J]. 地质通报, 2019, 38(12): 2022-2032 |
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| 作者姓名： | 李诗 陈建平 向杰 张志平 张烨 |
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| 作者单位： | 中国地质大学(北京)地球科学与资源学院, 北京 100083,中国地质大学(北京)地球科学与资源学院, 北京 100083,自然资源部成矿作用与资源评价重点实验室/中国地质科学院矿产资源研究所, 北京 100037,页岩气勘探开发国家地方联合工程研究中心(重庆地质矿产研究院), 重庆 401120,页岩气勘探开发国家地方联合工程研究中心(重庆地质矿产研究院), 重庆 401120 |
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| 基金项目： | 国家深地资源勘探开采专项《深部资源预测系统技术研究与示范》（编号：2017YFC0601502）、自然资源部公益性行业专项经费项目《地质大数据技术研究与应用试点》（编号：201511079-02）、重庆市社会事业与民生保障科技创新专项《富水断裂裂缝系统分布综合预测新技术研究与应用》（编号：cstc2017shmsA90003）和中国地质调查局项目《资源环境重大问题综合区划与开发保护策略研究》（编号：DD20190463） |
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| 摘 要： | 在大数据的时代背景下,地质大数据逐渐趋于复杂化的模式与其间的空间关联性为基于机器学习算法的矿产资源定量预测带来了更大的挑战。利用深度卷积网络算法优异的分析性能来提取不同成矿条件下多种二维要素图层的空间分布特征与关联性是一项非常有意义的探索性实验。以松桃—花垣地区沉积型锰矿为例,利用深度卷积神经网络模型AlexNet挖掘Mn元素、沉积相、大塘坡组出露、断裂及水系的空间分布与锰矿矿床的就位空间的耦合相关性,以及不同的控矿要素之间的相关性,以此训练出二维矿产预测分类模型。经过训练后,可以得到验证准确率88.89%,召回率为66.67%,损失值0.08的深度卷积神经网络分类模型。应用该模型对未知区进行二维成矿预测,共圈定出91、96、154、184号4个成矿远景区,其中91号和154号的区域含矿概率为1,96号含矿概率为0.5。由此可见,预测区具有很大概率存在尚未发现的矿床。
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| 关 键 词： | 大数据 找矿预测 卷积神经网络 Alextnet网络 松桃-花垣锰矿 |
| 收稿时间： | 2019-04-20 |
| 修稿时间： | 2019-07-23 |
Two-dimensional prospecting prediction based on AlexNet network: A case study of sedimentary Mn deposits in Songtao-Huayuan area |
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| LI Shi,CHEN Jianping,XIANG Jie,ZHANG Zhiping and ZHANG Ye. Two-dimensional prospecting prediction based on AlexNet network: A case study of sedimentary Mn deposits in Songtao-Huayuan area[J]. Geologcal Bulletin of China, 2019, 38(12): 2022-2032 |
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| Authors: | LI Shi CHEN Jianping XIANG Jie ZHANG Zhiping ZHANG Ye |
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| Affiliation: | School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China,School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China,Key Laboratory of Metallogeny and Mineral Assessment, MNR/Institute of Mineral Resources, CAGS, Beijing 100037, China,National and Local Joint Engineering Research Center for Shale Gas Exploration and Development(Chongqing Institute of Geology and Mineral Resources), Chongqing 401120, China and National and Local Joint Engineering Research Center for Shale Gas Exploration and Development(Chongqing Institute of Geology and Mineral Resources), Chongqing 401120, China |
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| Abstract: | There are many challenges in the task of predicting ore deposits from big data repositories. The data are inherently complex and have great significance to the intervenient spatial relevance of deposits. The characteristics of the data make it difficult to use machine learning algorithms for the quantitative prediction of mineral resources. There are considerable interest and value in extracting spatial distribution characteristics from two-dimensional ore-controlling factors''layers under different metallogenic conditions. In this paper, the authors conducted such an analysis by using a Deep Convolutional Neural Network (D-CNN) algorithm named AlexNet. Training on the two-dimensional (2-d) mineral prediction and classification model was performed using data from the Songtao-Huayuan sedimentary manganese deposit. The authors investigated the coupling correlation between the spatial distribution of manganese element, sedimentary facies, outcrop of Datangpo Formation, faults, water system and the areas where manganese orebodies are present, as well as the correlation between different ore-controlling factors by employing the AlexNet networks. After training, the deep convolutional neural network classification model with the verification accuracy of 88.89%, recall of 66.67% and loss value of 0.08 could be obtained. By applying this model to unknown areas for two-dimensional metallogenic prediction, four metallogenic prospective areas. i.e., No. 91, No. 96, No. 154 and No. 184, were delineated, in which the ore potential probability of No. 91 regional ore-bearing probability and No. 154 prospective area is 1, and that of No. 96 is 0.5, suggesting that the probability of existence of undiscovered deposits in prediction areas is large. |
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| Keywords: | big data prospecting prediction convolutional neural network Alextnet network Songtao-Huayuan Mn deposits |
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