• DocumentCode
    3511938
  • Title

    Mass anomaly depth estimation from Full Tensor Gradient gravity data

  • Author

    Wedge, D.

  • Author_Institution
    Centre for Exploration Targeting, Univ. of Western Australia, Crawley, WA, Australia
  • fYear
    2013
  • fDate
    15-17 Jan. 2013
  • Firstpage
    526
  • Lastpage
    533
  • Abstract
    Full Tensor Gradient (FTG) gravity data measures the derivatives of the Earth´s gravitational field. Such variations in the gravitational field may be due to the presence of bodies of higher or lower density relative to the surrounding rock. Recent technological advances have made airborne measurement of FTG data possible, resulting in the rapid collection of vast quantites of data particularly for mineral, oil and gas exploration purposes. We introduce an algorithm that uses an accumulation method over a volume to determine the potential locations of mass anomalies. Lines are cast through the volume according to physical properties of the FTG tensor, and votes accumulated from all such lines. Local maxima of the volume coincide with mass anomalies. We evaluate our algorithm on synthetic FTG data where the depths of mass anomalies are known. A multi-scale approach is demonstrated to effectively locate the depths of mass anomalies in the presence of noise.
  • Keywords
    Earth; geophysical image processing; gravity; rocks; tensors; Earth gravitational field; FTG gravity data; accumulation method; full tensor gradient gravity data; gas exploration; mass anomaly depth estimation; mineral exploration; oil exploration; surrounding rock; Eigenvalues and eigenfunctions; Gravity; Noise; Robustness; Rocks; Shape; Tensile stress;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Applications of Computer Vision (WACV), 2013 IEEE Workshop on
  • Conference_Location
    Tampa, FL
  • ISSN
    1550-5790
  • Print_ISBN
    978-1-4673-5053-2
  • Electronic_ISBN
    1550-5790
  • Type

    conf

  • DOI
    10.1109/WACV.2013.6475064
  • Filename
    6475064