Journal of Jilin University(Earth Science Edition) ›› 2020, Vol. 50 ›› Issue (6): 1929-1938.doi: 10.13278/j.cnki.jjuese.20190209

Previous Articles    

Remote Sensing Geochemical Inversion Model by Using Extreme Learning Machine

Sun Liying1, Yang Chen1, Zhao Haishi2, Chang Zhiyong3,4   

  1. 1. College of Earth Sciences, Jilin University, Changchun 130061, China;
    2. College of Computer Science and Technology, Jinlin University, Changchun 130012, China;
    3. College of Biological and Agricultural Engineering, Jilin University/Key Laboratory of Bionic Engineering(Jilin University), Ministry of Education, Changchun 130022, China;
    4. National-Local Joint Engineering Laboratory of In-Situ Conversion, Drilling and Exploitation Technology for Oil Shale, Jilin University, Changchun 130021, China
  • Received:2019-08-02 Published:2020-12-11
  • Supported by:
    Supported by National Natural Science Foundation of China (61572228), Science-Technology Development Plan Project of Jilin Province of China (20190303006SF, 20190302107GX) and Industrial Innovation Special Funds Project of Jilin Province (2019C053-5, 2019C053-7)

PDF (PC)

227

Abstract: Geochemical exploration research involves a large amount of sampling work, which is extremely difficult in inaccessible terrain with harsh working environments. The authors propose a geochemical inversion model with remote sensing images by using extreme learning machine (ELM) to alleviate the difficulty of ore prospecting in the areas with insufficient regional data. The partial least squares regression (PLSR) method is used to select the remote sensing image features which are highly correlated with geochemistry data. In this model, the nonlinear relationship between the geochemical data and the remote sensing images is established using ELM for getting unknown geochemical anomalies, after which the ore prospecting work can be further promoted. In the experiment, 1:200 000 soil geochemical data of Cu element and the Landsat 8 OLI remote sensing images were used for the inversion analysis. The experimental results showed that the anomalous distribution obtained by the ELM-based inversion model had a good correspondence with known ore spots, which verified the effectiveness of the proposed model.

Key words: extreme learning machine (ELM), partial least squares regression, remote sensing geochemistry, inversion

CLC Number: 

  • TP751.1
[1] 王健. 基于地质统计学模拟的地球化学异常信息提取[D]. 武汉:中国地质大学, 2018. Wang Jian. Identification of Geochemical Anomalies Based on Geostatistical Simulation[D]. Wuhan:China University of Geosciences, 2018.
[2] Pieters C M, Englert P A. Remote Geochemical Analysis:Elemental and Mineralogical Composition[M]. New York:Cambridge University Press, 1993.
[3] 吴昀昭, 田庆久, 季峻峰, 等. 遥感地球化学研究[J]. 地球科学进展, 2003,18(2):228-235. Wu Yunzhao, Tian Qingjiu, Ji Junfeng, et al. Study on the Remote-Sensing Geochemistry[J]. Advance in Earth Science, 2003, 18(2):228-235.
[4] Swayze G A. The Hydrothermal and Structural History of the Cuprite Mining District, Southwestern Nevada:An Integrated Geological and Geophysical Approach[D]. Colorado:University of Colorado, 1997.
[5] 姚佛军, 杨建民, 陈红旗, 等. 西藏多龙矿集区铜元素遥感地球化学模型[J]. 岩石矿物学杂志, 2015, 34(5):710-720. Yao Fojun, Yang Jianmin, Chen Hongqi, et al. A Remote Sensing Cu Geochemical Model for the Duolong Ore Concentration Area, Tibet[J]. Acta Petrologica et Mineralogica, 2015, 34(5):710-720.
[6] Aronoff S, Goodfellow W. 图像处理对区域地球化学数据的有效分析[C]//王润生. 综合地学信息图像处理译文集. 北京:地质矿产部地质遥感中心, 1987:16-23. Aronoff S, Goodfellow W. Effective Analysis of Regional Geochemical Data by Image Processing[C]//Wang Runsheng. Integrated Geological Information Image Processing Translation Collection. Beijing:Geological Remote Sensing Center, Ministry of Geology and Mineral Resources, 1987:16-23.
[7] Eliason P T, Donovan T J, Chavez P S. Integration of Geologic, Geochemical, and Geophysical Data of the Cement Oil Field, Oklahoma, Using Spatial Array Processing[J]. Geophysics, 1982, 1(1):474-475.
[8] 吕凤军, 李锌铭, 张应刚, 等. 冀西北银地球化学块体与遥感成矿信息集成研究[J]. 地质学刊, 2014, 38(2):259-263. Lü Fengjun, Li Xinming, Zhang Yinggang, et al.Study on Silver Geochemistry Block and Remote Sensing Metallogenic Information Integration in Northwest Hebei[J]. Journal of Geology, 2014, 38(2):259-263.
[9] 陈勇敢, 王美娟, 李鹏. 基于HJ-1A-HSI提取稀土元素地球化学异常信息研究[J]. 光谱学与光谱分析, 2015, 35(11):3172-3175. Chen Yonggan, Wang Meijuan, Li Peng. Study on the Geochemical Anomalies Identification of REE Based on HJ-1A-HSI[J]. Spectroscopy and Spectral Analysis, 2015, 35(11):3172-3175.
[10] Lucey P G, Blewett D T, Hawke B R. Mapping the FeO and TiO2, Content of the Lunar Surface with Multispectral Imagery[J]. Journal of Geophysical Research Planets, 1998, 103(E2):3679-3699.
[11] 周贤锋, 赵书河, 吴昀昭, 等. 基于光学遥感数据虹湾地区铁钛含量反演研究[J]. 中国科学:物理学力学天文学, 2013, 43(8):987-1003. Zhou Xianfeng, Zhao Shuhe, Wu Yunzhao, et al. Research on Extraction of FeO and TiO2 Contents of the Sinus Iridum Region Based on Optical Remote Sensing Data[J]. Scientia Sinica:Physica, Mechanica & Astronomica, 2013, 43(8):987-1003.
[12] 凌宗成, 张江, 刘建忠, 等. 嫦娥一号干涉成像光谱仪数据再校正与全月铁钛元素反演[J]. 岩石学报, 2016, 32(1):87-98. Ling Zongcheng, Zhang Jiang, Liu Jianzhong,et al. Lunar Global FeO and TiO2 Mapping Based on the Recalibrated Chang'e-1 IIM Dataset[J]. Acta Petrologica Sinica, 2016, 32(1):87-98.
[13] 李晓芃, 陈建平, 王翔. 基于嫦娥一号反射率数据月表正面FeO、Al2O3反演[J]. 中国矿业, 2018, 27(7):150-156. Li Xiaopeng, Chen Jianping, Wang Xiang.Inversion of Lunar Nearside FeO and Al2O3 Based on Chang'e-1 Reflectance Data[J]. China Mining Magazine, 2018, 27(7):150-156.
[14] Li S, Li L, Milliken R, et al. Hybridization of Partial Least Squares and Neural Network Models for Quantifying Lunar Surface Minerals[J]. Icarus, 2012, 221(1):208-225.
[15] 赵海士. 基于ETM+的遥感地球化学非线性反演模型研究[D]. 长春:吉林大学, 2017. Zhao Haishi. Research on a Remote-Sensing Geochemistry Nonlinear Inversion Model Based on ETM+ Data[D]. Changchun:Jilin University, 2017.
[16] Huang G B, Zhu Q Y, Siew C K. Extreme Learning Machine:Theory and Applications[J]. Neurocomputing, 2006, 70(1):489-501.
[17] Huang G B, Wang D H, Lan Y. Extreme Learning Machines:A Survey[J]. International Journal of Machine Learning and Cybernetics, 2011, 2(2):107-122.
[18] Huang G B, Zhou H, Ding X, et al. Extreme Learning Machine for Regression and Multiclass Classification[J], IEEE Transactions on Systems Man & Cybernetics Part B, 2012, 42(2):513.
[19] 王明常, 张馨月, 张旭晴, 等. 基于极限学习机的GF-2影像分类[J]. 吉林大学学报(地球科学版), 2018, 48(2):373-378. Wang Mingchang, Zhang Xinyue, Zhang Xuqing, et al. GF-2 Image Classification Based on Extreme Learning Machine[J]. Journal of Jilin University (Earth Science Edition), 2018, 48(2):373-378.
[20] 张守林. 基于ETM数据矿化蚀变信息定量提取方法研究[D]. 北京:中国地质大学(北京), 2006. Zhang Shoulin. A Study on Methods Used to Quantitatively Extract Mineralized Alteration Information from ETM Data[D]. Beijing:China University of Geosciences (Beijing), 2006.
[21] 吴应瑞, 吴道夫, 张兰喜, 等. 三道桥幅K-48-291:20万区域地质调查报告[R]. 北京:全国地质资料馆, 1982. Wu Yingrui, Wu Daofu, Zhang Lanxi, et al. Three-Way Bridge K-48-291:200000 Regional Geological Survey Report[R]. Beijing:National Geological Data Center, 1982.
[22] 刘永顺, 周世军, 杨俊泉,等. 内蒙古1:5万那仁宝力格幅、瑙云乌苏幅、希宁乌苏庙幅、玻璃庙幅区域地质调查报告[R]. 北京:全国地质资料馆, 2011. Liu Yongshun, Zhou Shijun, Yang Junquan, et al. Inner Mongolia 1:50000 Renbao Lige, Nayun Wusu, Xining Wusu Temple, Glass Temple Regional Geological Survey Report[R]. Beijing:National Geological Data Center, 2011.
[23] 冯翼鹏, 张维杰, 王根厚, 等. 阿拉善银根-额济纳盆地苏红图火山机构特征及火山喷发作用演化[J]. 矿物岩石地球化学通报, 2019,38(24):1-10. Feng Yipeng, Zhang Weijie, Wang Genhou, et al.Evolution of the Suhongtu Volcanic Edifice and Volcanic Effusive Activity in the Inngen-Ejin Qi Basin, Alxa[J]. Bulletin of Mineralogy, Petrology and Geochemistry, 2019, 38(24):1-10.
[1] Wang Tiexing, Wang Deli, Sun Jing, Hu bin, Liu Sixiu. Separation and Primary Estimation of Blended Data by 3D Sparse Inversion [J]. Journal of Jilin University(Earth Science Edition), 2020, 50(3): 895-904.
[2] Chen Shoutian, Wu Yujin, Fu Xichun, Zhang Wenlong, An Zhaohui, Cong Peihong. Fine Portray of Thin Sand-Bodies and Trajectory Design of Horizontal Well in Zhaoyuan Area [J]. Journal of Jilin University(Earth Science Edition), 2020, 50(2): 598-607.
[3] Yan Yingwei, Wang Zhejiang, Han Fei, Liu Cong, Zeng Fanjie. Nonlinear Inversion of Multi-Mode Surface Waves [J]. Journal of Jilin University(Earth Science Edition), 2019, 49(6): 1768-1779.
[4] Luo Teng, Feng Xuan, Guo Zhiqi, Liu Cai, Liu Xiwu. Seismic Inversion of Anisotropy Parameters of Fractured Reservoirs by Simulated Annealing and Particle Swarm Optimization [J]. Journal of Jilin University(Earth Science Edition), 2019, 49(5): 1466-1476.
[5] Yang Yue, Weng Aihua, Zhang Yanhui, Li Shiwen, Li Jianping, Tang Yu. Three-Dimensional Inversion Based on the Impedance Information of Controlled Source Electromagnetic Method by Limited Memory Quasi-Newton Method [J]. Journal of Jilin University(Earth Science Edition), 2019, 49(2): 591-602.
[6] Liang Shengxian. A Self-Constrained 3D Inversion and Efficient Solution of Gravity Data Based on Cross-Correlation Coefficient [J]. Journal of Jilin University(Earth Science Edition), 2018, 48(5): 1473-1482.
[7] Zheng Guolei, Xu Xinxue, Li Shibin, Yuan Hang, Ma Wei, Ye Qing. Inversion of Gravity Data in Tianjin [J]. Journal of Jilin University(Earth Science Edition), 2018, 48(4): 1221-1230.
[8] Wang Taihan, Huang Danian, Ma Guoqing, Li Ye, Lin Song. Three-Dimensional Fast Gravity Inversion Using Parallel Preconditioned Algorithm [J]. Journal of Jilin University(Earth Science Edition), 2018, 48(2): 384-393.
[9] Liu Xintong, Liu Sixin, Meng Xu, Fu Lei. Envelope Waveform Inversion of Cross-Hole Radar Without Low Frequency Data [J]. Journal of Jilin University(Earth Science Edition), 2018, 48(2): 474-482.
[10] Weng Aihua, Li Sirui, Yang Yue, Li Dajun, Li Jianping, Li Shiwen. Basic Principle, Current Status and Prospect of Magnetometric Resistivity [J]. Journal of Jilin University(Earth Science Edition), 2017, 47(6): 1838-1854.
[11] Wang Tong, Wang Deli, Feng Fei, Cheng Hao, Wei Jingxuan, Tian Mi. Multiple Prediction with 3D Sparse Inversion and Curvelet Match [J]. Journal of Jilin University(Earth Science Edition), 2017, 47(6): 1865-1874.
[12] Chen Shuai, Li Tonglin, Zhang Rongzhe. 1-D Occam Inversion of Transient Electromagnetic in Consideration of Induced Polarization Effect [J]. Journal of Jilin University(Earth Science Edition), 2017, 47(4): 1278-1285.
[13] Zhang Dailei, Huang Danian, Zhang Chong. Application of BP Neural Network Based on Genetic Algorithm in the Inversion of Density Interface [J]. Journal of Jilin University(Earth Science Edition), 2017, 47(2): 580-588.
[14] Gao Xiuhe, Huang Danian, Sun Siyuan, Yu Ping. Identify the Dip Angle of the Dipping Dike Model Based on Cokriging Inversion of Gravity Gradient Data [J]. Journal of Jilin University(Earth Science Edition), 2017, 47(2): 589-596.
[15] Weng Aihua, Liu Jiayin, Jia Dingyu, Yang Yue, Li Jianping, Li Yabin, Zhao Xiangyang. 1-D Inversion for Controlled Source Electromagnetic Sounding Using Limited Memory Quasi-Newton Method [J]. Journal of Jilin University(Earth Science Edition), 2017, 47(2): 597-605.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Earth Science Edition), All Rights Reserved.
Tel: +86-431-88502374；13756165364　E-mail: xuebao1956@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd