Temporal Orthogonal Projection Inversion for EMI Sensing of UXO

被引:14
|
作者
Song, Lin-Ping [1 ]
Oldenburg, Douglas W. [1 ]
Pasion, L. R. [2 ]
Billings, S. D. [2 ]
Beran, L. [2 ]
机构
[1] Univ British Columbia, Dept Earth Ocean & Atmospher Sci, Vancouver, BC V6T 1Z4, Canada
[2] Black Tusk Geophys Inc, Vancouver, BC V6J 4S5, Canada
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2015年 / 53卷 / 02期
关键词
Electromagnetic induction (EMI); magnetic dipole polarization; nonlinear inversion; orthogonal projection; subspace; unexploded ordnance (UXO); UNEXPLODED ORDNANCE; DISCRIMINATION; CLASSIFICATION; IDENTIFICATION; POLARIZABILITIES; OBJECTS; MODEL;
D O I
10.1109/TGRS.2014.2332992
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
We present a new approach for inverting time-domain electromagnetic data to recover the location and magnetic dipole polarizations of a limited number of buried objects. We form the multichannel electromagnetic induction (EMI) sensor data as a spatial-temporal response matrix (STRM). The rows of the STRM correspond to measurements sampled at different time channels from one sensor and the columns correspond to measurements sampled at the same time channel from different sensors. The singular value decomposition of the STRM produces the left and right singular vectors that are related to the sensor and the temporal spaces, respectively. If the effective rank of the STRM is r, then the first r singular vectors span signal subspaces (SS), and the remaining singular vectors span the noise subspaces. The original data are projected onto the SS, and the temporal orthogonal projection inversion (TOPI) uses these data in a nonlinear inverse problem to solve for source locations of the objects. The polarizations of the targets are then obtained by solving a linear optimization problem in the original data domain. We present theoretical and numerical analyses to investigate the singular value system of the STRM and the sensitivity of the TOPI to the size of an SS. Only a few subspace vectors are required to generate locations of the objects. The results are insensitive to the exact choice of rank, and this differs from usual methods that involve selecting the number of time channels to be used in the inversion and carefully estimating associated uncertainties. The proposed approach is evaluated using the synthetic and real multistatic EMI data.
引用
收藏
页码:1061 / 1072
页数:12
相关论文
共 50 条
  • [21] Orthogonal projection regularization operators
    Morigi, S.
    Reichel, L.
    Sgallari, F.
    NUMERICAL ALGORITHMS, 2007, 44 (02) : 99 - 114
  • [22] Feedback Spatial-Temporal Infrared Small Target Detection Based on Orthogonal Subspace Projection
    Luo, Yuan
    Li, Xiaorun
    Chen, Shuhan
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62 : 1 - 19
  • [23] Orthogonal Projection in Linear Bandits
    Kang, Qiyu
    Tay, Wee Peng
    2019 7TH IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (IEEE GLOBALSIP), 2019,
  • [24] Orthogonal projection regularization operators
    S. Morigi
    L. Reichel
    F. Sgallari
    Numerical Algorithms, 2007, 44 : 99 - 114
  • [25] Sensor fusion performance gain for buried mine/UXO detection using GPR, EMI, and MAG sensors
    Marble, JA
    Ackenhusen, JG
    Wegrzyn, JW
    Mancuso, J
    Dwan, C
    DETECTION AND REMEDIATION TECHNOLOGIES FOR MINES AND MINELIKE TARGETS V, PTS 1 AND 2, 2000, 4038 : 1473 - 1484
  • [26] Signal Power Estimation Based on Orthogonal Projection and Oblique Projection
    Suga, Norisato
    Furukawa, Toshihiro
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2016, E99A (12): : 2571 - 2575
  • [27] The Orthogonal Projection and the Riesz Representation Theorem
    Narita, Keiko
    Endou, Noboru
    Shidama, Yasunari
    FORMALIZED MATHEMATICS, 2015, 23 (03): : 243 - 252
  • [28] Fast Orthogonal Projection for Hyperspectral Unmixing
    Tao, Xuanwen
    Paoletti, Mercedes E.
    Han, Lirong
    Haut, Juan M.
    Ren, Peng
    Plaza, Javier
    Plaza, Antonio
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [29] New bounds for perturbation of the orthogonal projection
    Li, Bingxiang
    Li, Wen
    Cui, Lubin
    CALCOLO, 2013, 50 (01) : 69 - 78
  • [30] New bounds for perturbation of the orthogonal projection
    Bingxiang Li
    Wen Li
    Lubin Cui
    Calcolo, 2013, 50 : 69 - 78