
TY  - JOUR
A1  - Pilkington, Mark 
A1  - Keating, Pierre 
T1  - Contact mapping from gridded magnetic data - a comparison of techniques
JF  - ASEG Extended Abstracts
JO  - ASEG Extended Abstracts
Y1  - 2004
VL  - 2004
IS  - 1
SP  - 1
EP  - 4
KW  - 
UR  - https://doi.org/10.1071/ASEG2004AB113 
N2  - Delineating the edges of magnetised bodies is a fundamental application of magnetic data to geological mapping in areas of limited exposure. Especially in Precambrian shield-like regions, locating lateral changes in magnetisation of the outcropping crystalline rocks provides spatial information that is crucial in extending mapped geology into sparsely exposed or completely covered areas. Although not all magnetic contacts correspond to lithological contacts, the former provide key information on structural regimes, deformation styles and trends, and magnetic texture. Many techniques for contact mapping have been developed, some originally based on profile (2-D) data and others designed specifically for grid-based (3-D) data sets. Here, we evaluate five methods applied to gridded data. The first three are based on finding maxima of the horizontal gradient magnitude of the total field (TF-hgm), tilt (TI-hgm) and pseudogravity (PSG-hgm). The fourth and fifth methods rely on locating maxima of the analytic signal (AS) and the 3-D local wavenumber (LW). Method TF-hgm produces theoretically correct contact locations only when the data are reduced to the pole, and even then may produce false or secondary solutions mimicking contact trends. Method TI-hgm is less sensitive to field direction but also suffers from secondary maxima. Method PSG-hgm is perhaps the most established approach of those mentioned, and in the case of vertical contacts produces reliable maxima. However, knowledge of remanent magnetization direction is required. Methods AS and LW theoretically produce maxima directly over contacts and are insensitive to magnetisation direction but are more sensitive to noise than the former, which limits their application to higher quality datasets. 
ER  -

