3D resistivity gridding of large AEM datasets: A step toward enhanced geological interpretation

被引:22
|
作者
Pryet, Alexandre [1 ]
Ramm, James [2 ]
Chiles, Jean-Paul [3 ]
Auken, Esben [2 ]
Deffontaines, Benoit [4 ]
Violette, Sophie [1 ]
机构
[1] Univ Paris 06, CNRS, UMR Sisyphe, F-75252 Paris 05, France
[2] Univ Aarhus, HydroGeophys Grp, Dept Earth Sci, DK-8000 Aarhus C, Denmark
[3] MINES ParisTech, Ctr Geosci & Geoengn, Fontainebleau, France
[4] UPE, GTMC Lab, Marne La Vallee, France
关键词
Airborne electromagnetics; 3D resistivity model; 3D grid; Kriging; Interpolation; AIRBORNE ELECTROMAGNETIC DATA; CONSTRAINED INVERSION; TEM DATA; AUSTRALIA;
D O I
10.1016/j.jappgeo.2011.07.006
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
We develop a technique allowing 3D gridding of large sets of 1D resistivity models obtained after inversion of extensive airborne EM surveys. The method is based on the assumption of a layered-earth model. 2D kriging is used for interpolation of geophysical model parameters and their corresponding uncertainties. The 3D grid is created from the interpolated data, its structure accurately follows the geophysical model, providing a lightweight file for a good rendering. Propagation of errors is tracked through the quantification of uncertainties from both inversion and interpolation procedures. The 3D grid is exported to a portable standard, which allows flexible visualization and volumetric computations, and improves interpretation. The method is validated and illustrated by a case-study on Santa Cruz Island, in the Galapagos Archipelago. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码:277 / 283
页数:7
相关论文
共 50 条
  • [21] Place Recognition using Keypoint Voting in Large 3D Lidar Datasets
    Bosse, Michael
    Zlot, Robert
    2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2013, : 2677 - 2684
  • [22] Application of Artificial Neural Networks in Lithofacies Interpretation Used for 3D Geological Modelling
    Ma, Xueping
    Zhang, Jinliang
    Zhao, Hongjuan
    2009 ISECS INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT, VOL IV, 2009, : 451 - 454
  • [23] 3D MAGNETIC MODEL KORSUN-NOVOMIRGOROD PLUTON AND ITS GEOLOGICAL INTERPRETATION
    Pashkevich, I. K.
    Bakarjieva, M. I.
    Marchenko, A. V.
    Lebed, T. V.
    GEODYNAMICS, 2013, (15): : 268 - 270
  • [24] Geological model of Lobodice undergroun d gas storage facility based on 3D seismic interpretation
    Kopal, Lukas
    Cizek, Pavel
    Milicka, Jan
    CONTRIBUTIONS TO GEOPHYSICS AND GEODESY, 2016, 46 (02): : 125 - 135
  • [25] Maximising the extraction of geological information from geophysical datasets using machine learning and 3D mapping
    Czertowicz, Tom
    Kovac, Peter
    Anderson, Helen
    16TH SGA BIENNIAL MEETING, 2022, VOL 1, 2022, : 77 - 80
  • [27] A stacking methodology of machine learning for 3D geological modeling with geological-geophysical datasets, Laochang Sn camp, Gejiu (China)
    Jia, Ran
    Lv, Yikai
    Wang, Gongwen
    Carranza, EmmanuelJohnM
    Chen, Yongqing
    Wei, Chao
    Zhang, Zhiqiang
    COMPUTERS & GEOSCIENCES, 2021, 151
  • [28] Slopes of an airborne electromagnetic resistivity model interpolated jointly with borehole data for 3D geological modelling
    Reninger, P. -A.
    Martelet, G.
    Perrin, J.
    Deparis, J.
    Chen, Y.
    GEOPHYSICAL PROSPECTING, 2017, 65 (04) : 1085 - 1096
  • [29] Possibilities of Interpretation of the Magnetotelluric Data, Obtained on a Single Profile over 3D Resistivity Structures
    Ivanov, P. V.
    Pushkarev, P. Yu.
    IZVESTIYA-PHYSICS OF THE SOLID EARTH, 2010, 46 (09) : 727 - 734
  • [30] Interpretation of resistivity data using 3D Euler deconvolution and Radially Averaged Power Spectrum
    Shovana Mondal
    Shalivahan Srivastava
    Ashok K Gupta
    Journal of Earth System Science, 2020, 129