| Citation: |
LI Shi, CHEN Jianping, XIANG Jie, ZHANG Zhiping, ZHANG Ye. Two-dimensional prospecting prediction based on AlexNet network: A case study of sedimentary Mn deposits in Songtao-Huayuan area[J]. Geological Bulletin of China, 2019, 38(12): 2022-2032.
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Two-dimensional prospecting prediction based on AlexNet network: A case study of sedimentary Mn deposits in Songtao-Huayuan area
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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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References
[1] 赵鹏大.大数据时代数字找矿与定量评价[J].地质通报, 2015, 34(7):1255-1259. doi: 10.3969/j.issn.1671-2552.2015.07.001 [2] 肖克炎, 孙莉, 李楠, 等.大数据思维下的矿产资源评价[J].地质通报, 2015, 34(7):1266-1272. doi: 10.3969/j.issn.1671-2552.2015.07.003 [3] 陈建平, 李婧, 崔宁, 等.大数据背景下地质云的构建与应用[J].地质通报, 2015, 34(7):1260-1265. doi: 10.3969/j.issn.1671-2552.2015.07.002 [4] 于萍萍, 陈建平, 柴福山等.基于地质大数据理念的模型驱动矿产资源定量预测[J].地质通报, 2015, 34(7):1333-1343. doi: 10.3969/j.issn.1671-2552.2015.07.011 [5] 郑啸, 李景朝, 王翔, 等.大数据背景下的国家地质信息服务系统建设[J].地质通报, 2015, 34(7):1316-1322. doi: 10.3969/j.issn.1671-2552.2015.07.009 [6] Zuo R G, Xiong Y H. Big data analytics of identifying geochemical anomalies supported by machine learning methods[J]. Natural Resources Research, 2018, 27(1):5-13. doi: 10.1007/s11053-017-9357-0 [7] Twarakavi N K C, Misra D, Bandopadhyay S. Prediction of arsenic in bedrock derived stream sediments at a gold mine site under conditions of sparse data[J]. Natural Resources Research, 2006, 15(1):15-26. doi: 10.1007/s11053-006-9013-6 [8] Chen M, Mao S W, Liu Y H. Big data:A survey[J]. Mobile Networks and Applications, 2014, 19(2):171-209. doi: 10.1007/s11036-013-0489-0 [9] O' Brien J J, Spry P G, Nettleton D, et al. Using Random Forests to distinguish gahnite compositions as an exploration guide to Broken Hill-type Pb-Zn-Ag deposits in the Broken Hill domain, Australia[J]. Journal of Geochemical Exploration, 2015, 149:74-86. doi: 10.1016/j.gexplo.2014.11.010 [10] Gonbadi A M, Tabatabaei S H, Carranza E J M. Supervised geochemical anomaly detection by pattern recognition[J]. Journal of Geochemical Exploration, 2015, 157:81-91. doi: 10.1016/j.gexplo.2015.06.001 [11] Kirkwood C, Cave M, Beamish D, et al. A machine learning approach to geochemical mapping[J]. Journal of Geochemical Exploration, 2016, 167:49-61. doi: 10.1016/j.gexplo.2016.05.003 [12] Zhao J N, Chen S Y, Zuo R G. Identifying geochemical anomalies associated with Au-Cu mineralization using multifractal and artificial neural network models in the Ningqiang district, Shaanxi, China[J]. Journal of Geochemical Exploration, 2016, 164:54-64. doi: 10.1016/j.gexplo.2015.06.018 [13] Xiong Y H, Zuo R G. Recognition of geochemical anomalies using a deep autoencoder network[J]. Computers & Geosciences, 2016, 86:75-82. [14] Chen Y L, Wu W. Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data[J]. Geochemistry:Exploration, Environment, Analysis, 2017, 17(3):231-238. doi: 10.1144/geochem2016-024 [15] 陈建平, 李靖, 谢帅, 等.中国地质大数据研究现状[J].地质学刊, 2017, 41(3):353-366. [16] 陈三明.锡矿山锑矿田多元地学综合信息成矿预测研究[D].中国地质大学(北京)博士学位论文, 2012: 1-290. http://cdmd.cnki.com.cn/Article/CDMD-11415-1012364487.htm [17] 陈剑平.基于MATLAB的神经网络模式识别技术在油气化探中的研究及应用[D].中国地质大学(北京)硕士学位论文, 2008: 1-49. http://cdmd.cnki.com.cn/Article/CDMD-11415-2008068717.htm [18] 杨浩.深度学习与主成分分析融合的研究与应用[D].成都理工大学硕士学位论文, 2016: 1-41. http://cdmd.cnki.com.cn/Article/CDMD-10616-1016224928.htm [19] Albora A M, Ucan O N, Ozmen A, et al. Separation of Bouguer anomaly map using cellular neural network[J]. Journal of Applied Geophysics, 2001, 46(2):129-142. doi: 10.1016/S0926-9851(01)00033-7 [20] 刘展, 刘茂诚, 魏巍, 等.基于细胞神经网络方法的重力异常分离[J].中国石油大学学报(自然科学版), 2010, 34(4):57-61. doi: 10.3969/j.issn.1673-5005.2010.04.010 [21] 李超, 江玉乐, 胡明科, 等.细胞神经网络在重力异常分异中的研究及应用[J].物探化探计算技术, 2015, 37(1):16-21. doi: 10.3969/j.issn.1001-1749.2015.01.03 [22] 刘艳鹏, 朱立新, 周永章.卷积神经网络及其在矿床找矿预测中的应用——以安徽省兆吉口铅锌矿床为例[J].岩石学报, 2018, 34(11):3217-3224. [23] Krizhevsky A, Sutskever I, Hinton G E. Image Net classification with deep convolutional neural networks[C]//Inter-national Conference on Neural Information ProcessingSystems. Curran Associates Inc., 2012: 1097-1105. https://dl.acm.org/doi/10.5555/2999134.2999257 [24] 周琦, 杜远生, 袁良军, 等.古天然气渗漏沉积型锰矿床找矿模型——以黔湘渝毗邻区南华纪"大塘坡式"锰矿为例[J].地质学报, 2017, 91(10):2285-2298. doi: 10.3969/j.issn.0001-5717.2017.10.010 [25] 刘巽锋, 王庆生, 高兴基, 等.贵州锰矿地质[M].贵阳:贵州人民出版社, 1989:1-191. [26] 王砚耕, 王兴来, 朱顺才.贵州东部大塘坡组地层沉积环境和成锰作用[M].贵阳:贵州人民出版社, 1985. [27] 王砚耕.一个浅海裂谷盆地的古老热水沉积锰矿——以武陵山震旦纪锰矿为例[J].岩相古地理, 1990, (1):38-45. [28] 赵东旭.震旦纪大塘坡期锰矿的内碎屑结构和重力流沉积[J].地质科学, 1990, (2):149-157. [29] 刘宝珺, 余光明, 陈成生.雅鲁藏布江缝合带日喀则群的蛇绿岩质海底扇及其板块构造意义[J].岩相古地理, 1993, 13(2):13-24. [30] 何明华.松桃及邻区早震旦世大塘坡早期岩相古地理及成锰条件[J].贵州地质, 1993, 10(1):62-67. [31] 何明华.黔东北及邻区早震旦世成锰期岩相古地理及菱锰矿矿床[J].沉积与特提斯地质, 2001, 21(3):39-47. doi: 10.3969/j.issn.1009-3850.2001.03.003 [32] 向文勤, 肖永开.铜仁-松桃地区南华系大塘坡式锰矿地质特征及成矿规律探讨[J].西南科技大学学报, 2013, 28(4):31-38. doi: 10.3969/j.issn.1671-8755.2013.04.006 [33] 牟军, 王安华, 黄道光.贵州松桃-印江地区"含锰岩系"沉积微相特征与远景预测[J].贵州地质, 2014, 31(2):99-104. doi: 10.3969/j.issn.1000-5943.2014.02.005 [34] 匡文龙, 李雪宇, 杨绍祥.湘西北地区民乐式锰矿成矿地质特征及矿床成因[J].地质科学, 2014, 49(1):305-323. doi: 10.3969/j.issn.0563-5020.2014.01.022 [35] Hobert J P. The data augmentation algorithm: Theory and methodology[C]//Brooks S, Gelman A, Jones G, et al. Handbook of markov chain monte carlo. Boca Raton: CRC Press, 2011: 253-293. [36] Salehinejad H, Valaee S, Dowdell T, et al. Image augmentation using radial transform for training deep neural networks[C]//IEEE. Proceedings of the 2018 IEEE international conference on acoustics, speech and signal processing. Piscataway: IEEE Press, 2018: 3016-3020. https://arxiv.org/abs/1708.04347 -
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LI Shi, CHEN Jianping, XIANG Jie, ZHANG Zhiping, ZHANG Ye. Two-dimensional prospecting prediction based on AlexNet network: A case study of sedimentary Mn deposits in Songtao-Huayuan area[J]. Geological Bulletin of China, 2019, 38(12): 2022-2032.
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Figure 1.
Lithofacies-palaeogeographic map of Songtao–Huayuan area during the Datangpo Stage of Nanhua Period
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Figure 2.
Technical framework of AlexNet
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Figure 3.
Preprocessing of two-dimensional data
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Figure 4.
Network structure of prediction model (a) and Hyper-parameter setting of AlexNet model (b)
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Figure 5.
Distribution and regional code of positive and negative samples
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Figure 6.
Superposition of prediction result and each ore-controlling element layer