Research on Geological Information Collection Based on Data Mining

Abstract

In order to improve the efficiency of collecting geological data and digitization, the geological data acquisition system based on WorldWind mobile terminal is developed, which integrates WorldWind map technology, network communication technology, multithread technology, and data storage technology. The realization methods of location module, data input module, solid projection module, and 3D display module are expounded. The research and development of this system realizes the information collection, browsing, and display of geological work, provides convenient data input mode for geological workers, and promotes the development of digitization, information, and modernization of geological and mineral work.

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References

  1. Cheng J, Mai X, Wang S (2018) Research on abnormal data mining algorithm based on ICA. Cluster Computing (4):1–7

  2. Huang C, Xia H, Hu J (2019a) Surface deformation monitoring in coal mine area based on psi. IEEE Access:29672–29678

  3. Huang C, Xia H, Hu J (2019b) Surface deformation monitoring in coal mine area based on psi. IEEE Access:29672–29678

  4. Jianxi Z, Changfeng Z, Huaizhi Y, Heled J, Yuan A (2018) Research on e-commerce intelligent service based on data mining. MATEC Web of Conferences 173:03012

    Article  Google Scholar 

  5. Kim JC, Jung HS, Lee S (2019) Spatial mapping of the groundwater potential of the Geum river basin using ensemble models based on remote sensing images. Remote Sensing 11(19):2285–2285

    Article  Google Scholar 

  6. Lee YS, Wang JR, Zhan JW, Zhang JM (2020) Data mining analysis of overall team information based on internet of things. IEEE Access (99):1-1

  7. Long, Y. . (2018). Research on art innovation teaching platform based on data mining algorithm. Cluster Computing(2), 1-7.

  8. Oparin VN, Kiryaeva TA, Potapov VP (2018) Methods and models for analyzing methane sorption capacity of coal based on its physicochemical characteristics. Journal of Mining ence 53(4):614–629

    Google Scholar 

  9. Sun W, Shi M, Zhang C, Zhao J, Song X (2018) Dynamic load prediction of tunnel boring machine (tbm) based on heterogeneous in-situ data. Automation in Construction 92(AUG.):23–34

    Article  Google Scholar 

  10. Tang X (2020) Research on logistics information collection based on image recognition. IEEE Access (99):1-1

  11. Tuan LB, Dong X, Yachun M, Dakuo H, Shengyong Z, Xiaoyu S et al (2018a) Coal exploration based on a multilayer extreme learning machine and satellite images. IEEE Access:1-1

  12. Tuan LB, Dong X, Yachun M, Dakuo H, Shengyong Z, Xiaoyu S et al (2018b) Coal exploration based on a multilayer extreme learning machine and satellite images. IEEE Access:1-1

  13. Vallee MA, Farquharson CG, Morris WA, King J, Byrne K, Lesage G et al (2019) Comparison of geophysical inversion programs run on aeromagnetic data collected over the highland valley copper district, British Columbia, Canada. Exploration Geophysics 50(3):310–323

    Article  Google Scholar 

  14. Wang Z, Ma J (2018) Layer-constrained triangulated irregular network algorithm based on ground penetrating radar data and its application. Journal of Beijing Institute of Technology 27(95(01)):150–158

    Google Scholar 

  15. Wang, R. , Ji, W. , Liu, M. , Wang, X. , Weng, J. , & Deng, S. , et al. (2018). Review on mining data from multiple data sources. Pattern Recognition Letters, 109(JUL.15), 120-128.

  16. Wu S, Wang M, Zou Y (2018) Research on internet information mining based on agent algorithm. Future Generation Computer Systems 86(SEP.):598–602

    Article  Google Scholar 

  17. Xia X, Chen Z, Wei W (2018) Research on monitoring and prewarning system of accident in the coal mine based on big data. Scientific Programming 2018, (2018-3-6), 2018(PT.1):1–10

    Article  Google Scholar 

  18. Xue Y, Dang F, Liu F, Li R, Ranjith PG, Wang S et al (2018a) An elastoplastic model for gas flow characteristics around drainage borehole considering post-peak failure and elastic compaction. Environmental Earth Sciences 77(19):669.1–669.18

    Article  Google Scholar 

  19. Xue S, Liu Y, Liu S, Li W, Wu Y, Pei Y (2018b) Numerical simulation for groundwater distribution after mining in Zhuanlongwan mining area based on visual MODFLOW. Environmental Earth ences 77(11):400

    Article  Google Scholar 

  20. Yang G, Lu L (2018) Simulation research for telecommunication data mining based on mobile information node. IET Software 12(3):245–250

    Article  Google Scholar 

  21. Yuanzhe L, Chaowei W, Yue Z (2018) Research on an improved approach for network security detection based on data mining and prefixspan algorithm simulation experiment. International Journal for Engineering Modelling 31(1):184–193

    Google Scholar 

  22. Zhou C, Ding LY, Skibniewski MJ, Luo H, Zhang HT (2018) Data based complex network modeling and analysis of shield tunneling performance in metro construction. Advanced engineering informatics 38(OCT.):168–186

    Article  Google Scholar 

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Correspondence to Qi Zheng.

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The author(s) declare that they have no competing interest.

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This article is part of the Topical Collection on Geological Modeling and Geospatial Data Analysis

Responsible Editor: Keda Cai

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Zheng, Q., Mok, C. Research on Geological Information Collection Based on Data Mining. Arab J Geosci 14, 300 (2021). https://doi.org/10.1007/s12517-021-06584-8

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Keywords

  • Mobile GIS
  • Android
  • WorldWind
  • Geological data collection