Advanced Search

Indexed by SCI、CA、РЖ、PA、CSA、ZR、etc .

Volume 32 Issue 2
Apr 2021
Turn off MathJax
Article Contents
Yongliang Chen, Shicheng Wang, Qingying Zhao, Guosheng Sun. Detection of Multivariate Geochemical Anomalies Using the Bat-Optimized Isolation Forest and Bat-Optimized Elliptic Envelope Models. Journal of Earth Science, 2021, 32(2): 415-426. doi: 10.1007/s12583-021-1402-6
Citation: Yongliang Chen, Shicheng Wang, Qingying Zhao, Guosheng Sun. Detection of Multivariate Geochemical Anomalies Using the Bat-Optimized Isolation Forest and Bat-Optimized Elliptic Envelope Models. Journal of Earth Science, 2021, 32(2): 415-426. doi: 10.1007/s12583-021-1402-6

Detection of Multivariate Geochemical Anomalies Using the Bat-Optimized Isolation Forest and Bat-Optimized Elliptic Envelope Models

doi: 10.1007/s12583-021-1402-6
More Information
  • Corresponding author: Chen Yongliang, chenyongliang2009@hotmail.com
  • Received Date: 23 Sep 2020
  • Accepted Date: 27 Nov 2020
  • Publish Date: 01 Apr 2021
  • Isolation forest and elliptic envelope are used to detect geochemical anomalies, and the bat algorithm was adopted to optimize the parameters of the two models. The two bat-optimized models and their default-parameter counterparts were used to detect multivariate geochemical anomalies from the stream sediment survey data of 1:50 000 scale collected from the Helong district, Jilin Province, China. Based on the data modeling results, the receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of the two bat-optimized models and their default-parameter counterparts. The results show that the bat algorithm can improve the performance of the two models by optimizing their parameters in geochemical anomaly detection. The optimal threshold determined by the Youden index was used to identify geochemical anomalies from the geochemical data points. Compared with the anomalies detected by the elliptic envelope models, the anomalies detected by the isolation forest models have higher spatial relationship with the mineral occurrences discovered in the study area. According to the results of this study and previous work, it can be inferred that the background population of the study area is complex, which is not suitable for the establishment of elliptic envelope model.

     

  • loading
  • Breunig, M. M., Kriegel, H. P., Ng, R. T., et al., 2000. LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Conference 2000, Dallas
    Chai, S. L., Liu, Z. H., 2015. Experimental Demonstration on 1: 50000 Scale Mineral Geology Survey of Four Geological Maps in the Helong Area, Jilin Province. Mineral Geology Survey Report (Internal Communication), Jilin University, Changchun. 205(in Chinese)
    Chen, Y. L., Lu, L. J., Li, X. B., 2014a. Kernel Mahalanobis Distance for Multivariate Geochemical Anomaly Recognition. Journal of Jilin University (Earth Science Edition), 44(1): 396-408(in Chinese) http://en.cnki.com.cn/Article_en/CJFDTOTAL-CCDZ201401040.htm
    Chen, Y. L., Lu, L. J., Li, X. B., 2014b. Application of Continuous Restricted Boltzmann Machine to Identify Multivariate Geochemical Anomaly. Journal of Geochemical Exploration, 140: 56-63. https://doi.org/10.1016/j.gexplo.2014.02.013
    Chen, Y. L., 2015. Mineral Potential Mapping with a Restricted Boltzmann Machine. Ore Geology Reviews, 71: 749-760. https://doi.org/10.1016/j.oregeorev.2014.08.012
    Chen, Y. L., Wu, W., 2016. A Prospecting Cost-Benefit Strategy for Mineral Potential Mapping Based on ROC Curve Analysis. Ore Geology Reviews, 74: 26-38. https://doi.org/10.1016/j.oregeorev.2015.11.011
    Chen, Y., Wu, W., 2017a. Mapping Mineral Prospectivity by Using One-Class Support Vector Machine to Identify Multivariate Geological Anomalies from Digital Geological Survey Data. Australian Journal of Earth Sciences, 64(5): 639-651. https://doi.org/10.1080/08120099.2017.1328705
    Chen, Y. L., Wu, W., 2017b. Mapping Mineral Prospectivity Using an Extreme Learning Machine Regression. Ore Geology Reviews, 80: 200-213. https://doi.org/10.1016/j.oregeorev.2016.06.033
    Chen, Y. L., Wu, W., 2017c. Application of One-Class Support Vector Machine to Quickly Identify Multivariate Anomalies from Geochemical Exploration Data. Geochemistry: Exploration, Environment, Analysis, 17(3): 231-238. https://doi.org/10.1144/geochem2016-024
    Chen, Y. L., Wu, W., 2019a. Isolation Forest as an Alternative Data-Driven Mineral Prospectivity Mapping Method with a Higher Data-Processing Efficiency. Natural Resources Research, 28(1): 31-46. https://doi.org/10.1007/s11053-018-9375-6
    Chen, Y. L., Wu, W., 2019b. Separation of Geochemical Anomalies from the Sample Data of Unknown Distribution Population Using Gaussian Mixture Model. Computers & Geosciences, 125: 9-18. https://doi.org/10.1016/j.cageo.2019.01.010
    Chen, Y. L., Wu, W., Zhao, Q. Y., 2019a. A Bat Algorithm-Based Data-Driven Model for Mineral Prospectivity Mapping. Natural Resources Research, 29(1): 247-265. https://doi.org/10.1007/s11053-019-09589-z
    Chen, Y. L., Wu, W., Zhao, Q. Y., 2019b. A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping. Minerals, 9(5): 317. https://doi.org/10.3390/min9050317
    Gałuszka, A., 2007. A Review of Geochemical Background Concepts and an Example Using Data from Poland. Environmental Geology, 52(5): 861-870. https://doi.org/10.1007/s00254-006-0528-2
    Goyal, S., Patterh, M. S., 2013. Wireless Sensor Network Localization Based on Bat Algorithm. International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), 4(5): 507-512
    Liu, F. S., Zhang, M. L., 1999. Complete Quality Management of the New-Round Land Resources Survey. Chinese Geology, 267(8): 20-21(in Chinese)
    Liu, F. T., Ting, K. M., Zhou, Z. H., 2008. Isolation Forest. Proceedings of the Eighth IEEE International Conference on Data Mining (ICDM), 413-422
    Pan, Y. D., Xu, B. J., Sun, Y., et al., 2016. Geological Features of the Jinchengdong Gold Deposit in Helong City, Jilin Province, China. Jilin Geology, 35(1): 30-35(in Chinese) http://en.cnki.com.cn/Article_en/CJFDTOTAL-JLDZ201601007.htm
    Rousseeuw, P. J., 1984. Least Median of Squares Regression. Journal of the American Statistical Association, 79(388): 871-880. https://doi.org/10.1080/01621459.1984.10477105
    Rousseeuw, P. J., van Driessen, K. V., 1999. A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3): 212-223. https://doi.org/10.1080/00401706.1999.10485670
    Sharawi, M., Emary, E., Saroit, I. A., et al., 2012. Bat Swarm Algorithm for Wireless Sensor Networks Lifetime Optimization. International Journal of Science and Research (IJSR), 3(5): 655-664 http://www.researchgate.net/publication/270241192_Bat_Swarm_Algorithm_for_Wireless_Sensor_Networks_Lifetime_Optimization
    Wan, W. Z., Wang, J. B., Feng, X. Y., et al., 2010. Geological Features and Prospecting Directions of the Heanhe Gold Deposit in the Helong Area, Jilin Province, China. Jilin Geology, 29(1): 71-75(in Chinese) http://en.cnki.com.cn/Article_en/CJFDTotal-JLDZ201004015.htm
    Wu, F., Lin, J., Wilde, S., et al., 2005. Nature and Significance of the Early Cretaceous Giant Igneous Event in Eastern China. Earth and Planetary Science Letters, 233(1/2): 103-119. https://doi.org/10.1016/j.epsl.2005.02.019
    Wu, P. F., Sun, D. Y., Wang, T. H., et al., 2013. Chronology, Geochemical Characteristic and Petrogenesis Analysis of Diorite in Helong of Yanbian Area, Northeastern China. Geological Journal of China Universities, 19(4): 600-610(in Chinese) http://en.cnki.com.cn/Article_en/CJFDTOTAL-GXDX201304006.htm
    Wu, W., Chen, Y. L., 2018. Application of Isolation Forest to extract Multivariate Anomalies from Geochemical Exploration Data. Global Geology, 21(1): 36-47. https://doi.org/10.3969/j.issn.1673-9736.2018.01.04
    Xiong, Y. H., Zuo, R. G., 2016. Recognition of Geochemical Anomalies Using a Deep Autoencoder Network. Computers & Geosciences, 86: 75-82. https://doi.org/10.1016/j.cageo.2015.10.006
    Yan, D., Li, N., Xu, M., et al., 2015. Mineralization Characteristics and Genesis of the Bailiping Silver Deposit in Helong City, Jilin Province. Jilin Geology, 34(3): 36-41(in Chinese) http://www.zhangqiaokeyan.com/academic-journal-cn_jilin-geology_thesis/0201253935009.html
    Yang, X. S., Gandomi, A. H., 2012. Bat Algorithm: A Novel Approach for Global Engineering Optimization. Engineering Computations, 29(5): 464-483. https://doi.org/10.1108/02644401211235834
    Yang, X. S., 2010. A new Metaheuristic Bat-Inspired Algorithm. In: Juan, R. G., David, A. P., Carlos, C., et al., eds., Nature Inspired Cooperative Strategies for Optimization. Springer-Verlag, Berlin. 65-74
    Yu, J. J., Wang, F., Xu, W. L., et al., 2012. Early Jurassic Mafic Magmatism in the Lesser Xing'an-Zhangguangcai Range, NE China, and Its Tectonic Implications: Constraints from Zircon U-Pb Chronology and Geochemistry. Lithos, 142/143:256-266. https://doi.org/10.1016/j.lithos.2012.03.016
    Zhang, Y. B., Wu, F. Y., Wilde, S. A., et al., 2004. Zircon U-Pb Ages and Tectonic Implications of 'Early Paleozoic' Granitoids at Yanbian, Jilin Province, Northeast China. The Island Arc, 13(4): 484-505. https://doi.org/10.1111/j.1440-1738.2004.00442.x
    Zheng, Z. Y., 2019. A Comparison between Several Machine Learning Methods for Multivariate Geochemical Anomaly Identification in the Helong Area, Jilin Province: [Dissertation]. Jilin University, Changchun. 40-50(in Chinese with English Abstract)
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(3)

    Article Metrics

    Article views(359) PDF downloads(24) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return