TY - JOUR
T1 - Automated lineament analysis of the Gairdner Dolerite dyke swarm of the Gawler Craton
AU - Pawley, Mark
AU - Irvine, John
AU - Melville, A.L.
AU - Krapf, Carmen
AU - Thiel, Stephane
AU - Gonzalez-Alvarez, Ignacio
AU - Kelka, Ulrich
AU - Martinez, Cericia
PY - 2021
Y1 - 2021
N2 - In extensively covered regions, such as the Gawler Craton, understanding crustal architecture and the relation between geological structures, lithologies and mineral systems is dependent upon remote-sensed data, such as aeromagnetics. However, the large-scale interpretation of geophysical datasets can be time consuming and subjective. In 2019–20 an innovative collaborative project between the Geological Survey of South Australia and CSIRO, aiming to link basement lineaments with surface lineaments (González Álvarezet al. 2020), resulted in the development of an automated lineament extraction and analysis workflow by Kelka and Martínez (2019). This methodology was applied successfully to an area in the central Gawler Craton (González-Álvarez et al. 2020; Kelka and Martínez 2019). This process allows the automatic generation of lineaments across large datasets in atime and cost-efficient way. Importantly, the generated lineament dataset includes derivative data, such as line length, orientation and line density, which can be used to analyse the data more objectively and statistically compare populations.
AB - In extensively covered regions, such as the Gawler Craton, understanding crustal architecture and the relation between geological structures, lithologies and mineral systems is dependent upon remote-sensed data, such as aeromagnetics. However, the large-scale interpretation of geophysical datasets can be time consuming and subjective. In 2019–20 an innovative collaborative project between the Geological Survey of South Australia and CSIRO, aiming to link basement lineaments with surface lineaments (González Álvarezet al. 2020), resulted in the development of an automated lineament extraction and analysis workflow by Kelka and Martínez (2019). This methodology was applied successfully to an area in the central Gawler Craton (González-Álvarez et al. 2020; Kelka and Martínez 2019). This process allows the automatic generation of lineaments across large datasets in atime and cost-efficient way. Importantly, the generated lineament dataset includes derivative data, such as line length, orientation and line density, which can be used to analyse the data more objectively and statistically compare populations.
M3 - Article
SN - 1326-3544
VL - 95
SP - 30
EP - 40
JO - MESA Journal
JF - MESA Journal
IS - 2
ER -