One method for probabilistic prediction of the material composition of deep crustal horizons using the geophysical data

被引:1
|
作者
Lelyaev, P. A. [1 ]
机构
[1] Russian Acad Sci, Schmidt Inst Phys Earth, Moscow 123995, Russia
关键词
algorithm; Earth's crust; seismic velocities; density;
D O I
10.1134/S1069351311080039
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Prediction of the material composition of deep crustal horizons in the Earth from the geophysical data requires an algorithm to classify the rocks according to their petrophysical properties. In the present work, we propose a classification algorithm that is based on the membership function and describe the computer program, which is based on this algorithm and intended for visualization of the most typical crystalline rocks of the Voronezh massif.
引用
收藏
页码:1083 / 1085
页数:3
相关论文
共 50 条
  • [1] One method for probabilistic prediction of the material composition of deep crustal horizons using the geophysical data
    P. A. Lelyaev
    Izvestiya, Physics of the Solid Earth, 2011, 47 : 1083 - 1085
  • [2] A Novel Method of Bridge Deflection Prediction Using Probabilistic Deep Learning and Measured Data
    Xiao, Xinhui
    Wang, Zepeng
    Zhang, Haiping
    Luo, Yuan
    Chen, Fanghuai
    Deng, Yang
    Lu, Naiwei
    Chen, Ying
    SENSORS, 2024, 24 (21)
  • [3] The arc of the western Alps in the light of geophysical data on deep crustal structure
    Schmid, SM
    Kissling, E
    TECTONICS, 2000, 19 (01) : 62 - 85
  • [4] Integrating geophysical data sets using probabilistic methods
    Pendock, N
    Nedeljkovic, V
    PROCEEDINGS OF THE ELEVENTH THEMATIC CONFERENCE - GEOLOGIC REMOTE SENSING: PRACTICAL SOLUTIONS FOR REAL WORLD PROBLEMS, VOL II, 1996, : 621 - 628
  • [5] Integrating geophysical data sets using probabilistic methods
    Pendock, N
    Nedeljkovic, V
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 1997, 18 (07) : 1627 - 1635
  • [6] Integrating geophysical data sets using probabilistic methods
    Dept of Comp + Applied Maths, Univ of Witswatersrand, PO Box 382, Wits 2050, Johannesburg, South Africa
    Int J Remote Sens, 7 (1627-1635):
  • [7] Numerical simulation of the crustal stress field by using data of deep geophysical exploration in the northern part of North China
    刘昌铨
    刘明军
    嘉世旭
    Acta Seismologica Sinica(English Edition), 1998, (03) : 18 - 29
  • [8] Deep learning to estimate permeability using geophysical data
    Mudunuru, M. K.
    Cromwell, E. L. D.
    Wang, H.
    Chen, X.
    ADVANCES IN WATER RESOURCES, 2022, 167
  • [9] Probabilistic inference of subsurface heterogeneity and interface geometry using geophysical data
    de Pasquale, G.
    Linde, N.
    Doetsch, J.
    Holbrook, W. S.
    GEOPHYSICAL JOURNAL INTERNATIONAL, 2019, 217 (02) : 816 - 831
  • [10] A method to construct statistical prior models of geology for probabilistic inversion of geophysical data
    Madsen, Rasmus Bodker
    Hoyer, Anne-Sophie
    Sandersen, Peter B. E.
    Moller, Ingelise
    Hansen, Thomas Mejer
    ENGINEERING GEOLOGY, 2023, 324