
TY  - JOUR
A1  - Dentith, M. 
A1  - Cowan, D.R. 
A1  - Tompkins, L.A. 
T1  - Enhancement of subtle features in aeromagnetic data
JF  - Exploration Geophysics
JO  - Explor. Geophys.
Y1  - 2000
VL  - 31
IS  - 2
SP  - 104
EP  - 108
KW  - 
UR  - https://doi.org/10.1071/EG00104 
N2  - The enhancement of magnetic data for qualitative interpretation involves manipulating magnetic relief and magnetic texture. Magnetic relief consists primarily of anomaly amplitude and is relatively objective. Magnetic texture consists of shape, size and continuity of adjacent anomalies and is more subjective. Conventional filtering responds primarily to amplitude variations within the data and high-amplitude anomalies often mask more subtle anomalies of interest. Changes in geology, or specifically rock magnetisation, within a survey area cause changes in both texture and relief. When rock magnetisation is weak, anomalies are subdued and particular filtering and enhancement methods are required. However, the effects of these on areas of greater magnetic relief must also be considered. Three fundamentally different approaches to enhancing subtle anomalies have been implemented and tested on a wide range of sedimentary basin and low magnetic gradient areas. The first approach uses separation or layer filtering to deconvolve the effects of magnetic sources around a mean depth. The second uses texture filtering using grey-level co-occurrence matrix (GLCM) based filters and the third uses the gradient tilt angle. The responses of these filters are illustrated using a regional aeromagnetic dataset from the Proterozoic Arunta block in the Northern Territory of Australia. Results show that each method has its advantages and limitations. Each shows different amplitude and wavelength responses for a given dataset, but all three are relatively broadband compared with a conventional filter such as a first vertical derivative. No single method performed well on all areas of the test dataset. The GLCM filters were almost pure texture with very limited tonal content. The gradient tilt angle provides good texture content, but retained more tonal content than the GLCM filters. Combining filters with different bandwidth such as the separation filter and tilt angle can be very effective and provides the best results. 
ER  -

