Determining structural boundaries using blocky magnetotelluric data inversion

被引:0
|
作者
Xu, Kaijun [1 ]
Li, Yaoguo [2 ]
机构
[1] China Univ Petr East China, Sch Geosci, Dept Geophys, Qingdao, Peoples R China
[2] Colorado Sch Mines, Ctr Grav Elect & Magnet Studies, Dept Geophys, Golden, CO USA
基金
中国国家自然科学基金;
关键词
boundary detection; electromagnetics; inversion; magnetotelluric; one dimensional; parameter estimation; resistivity; 3-DIMENSIONAL ELECTRICAL-RESISTIVITY; GEOTHERMAL RESOURCES; MODEL; CONDUCTIVITY; TRANSITION; ALGORITHM; INSIGHTS; SYSTEM; MT;
D O I
10.1111/1365-2478.70018
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
The magnetotelluric method has large depths of investigation and can provide important structural information in many exploration problems. The one-dimensional magnetotelluric inversion also has been applied to extract boundary information and provide the constraints for the interpretation of complementary datasets. Traditional smooth inversion based on the L(2 )norm only provides a single smooth model that it is difficult to detect the location of the geological boundary. Trans-dimensional inversion provides an effective means to determine the boundaries with uncertainty quantification but incurs significant computational costs. We present an efficient method to detect distinct interfaces from one-dimensional blocky magnetotelluric inversions using an Ekblom norm. The method leverages the Ekblom norm to assess the change in the recovered resistivity model with the threshold parameter as a means to delineate the significant boundaries in the subsurface. The threshold parameter specific to the Ekblom-norm inversion is then used to probe the variability of the inversion to obtain a more robust interface detection. Once the interfaces are detected, we calculate the average resistivity value between detected interfaces to form a final conductivity model. As a demonstration, we apply this method to a synthetic example and the field data from East Tennant in Australia. The results show that the method is effective in obtaining boundaries.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] ITERATIVE MOST SQUARES INVERSION OF MAGNETOTELLURIC DATA
    MEJU, MA
    HUTTON, VRS
    GEOPHYSICAL JOURNAL-OXFORD, 1988, 92 (03): : 524 - 524
  • [22] EFFICIENT INVERSION OF MAGNETOTELLURIC DATA IN 2 DIMENSIONS
    OLDENBURG, DW
    ELLIS, RG
    PHYSICS OF THE EARTH AND PLANETARY INTERIORS, 1993, 81 (1-4) : 177 - 200
  • [23] Analysis of prior models for a blocky inversion of seismic AVA data
    Theune, Ulrich
    Jensas, Ingrid Ostgard
    Eidsvik, Jo
    GEOPHYSICS, 2010, 75 (03) : C25 - C35
  • [24] 2D inversion of magnetotelluric data using deep learning technology
    Xiaolong Liao
    Zeyu Shi
    Zhihou Zhang
    Qixiang Yan
    Pengfei Liu
    Acta Geophysica, 2022, 70 : 1047 - 1060
  • [25] Estimating melt fraction in silicic systems using Bayesian inversion of magnetotelluric data
    Cordell, Darcy
    Hill, Graham
    Bachmann, Olivier
    Moorkamp, Max
    Huber, Christian
    JOURNAL OF VOLCANOLOGY AND GEOTHERMAL RESEARCH, 2022, 423
  • [26] Two-dimensional inversion of magnetotelluric/radiomagnetotelluric data by using unstructured mesh
    Ozyildirim, Ozcan
    Candansayar, Mehmet Emin
    Demirci, Ismail
    Tezkan, Bulent
    GEOPHYSICS, 2017, 82 (04) : E197 - E210
  • [27] Inversion for magnetotelluric data using the particle swarm optimization and regularized least squares
    Cui, Yi-an
    Zhang, Lijuan
    Zhu, Xiaoxiong
    Liu, Jianxin
    Guo, Zhenwei
    JOURNAL OF APPLIED GEOPHYSICS, 2020, 181
  • [28] Constrained joint inversion of magnetotelluric and seismic data using simulated annealing algorithm
    Yang, H
    Wang, JL
    Wu, JS
    Yu, P
    Wang, XM
    CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2002, 45 (05): : 723 - 734
  • [29] 2D inversion of magnetotelluric data using deep learning technology
    Liao, Xiaolong
    Shi, Zeyu
    Zhang, Zhihou
    Yan, Qixiang
    Liu, Pengfei
    ACTA GEOPHYSICA, 2022, 70 (03) : 1047 - 1060
  • [30] 1-Dimension magnetotelluric data inversion using MOEA/D algorithm
    Pramudiana
    Sungkono
    INTERNATIONAL CONFERENCE ON SCIENCE AND APPLIED SCIENCE (ENGINEERING AND EDUCATIONAL SCIENCE) 2016, 2017, 795