Airborne electromagnetic signature of the Beverley uranium deposit, South Australia

2006 ◽  
Author(s):  
Geoff McConachy ◽  
David Mcinnes ◽  
John Paine
Keyword(s):  
Uranium Deposit ◽  
South Australia ◽  
Airborne Electromagnetic ◽  
Electromagnetic Signature

scholarly journals The Frome Airborne Electromagnetic Survey: Uncovering 10% of South Australia

Preview ◽  
2012 ◽  
Vol 2012 (158) ◽  
pp. 39-42 ◽  
Author(s):  
Marina T. Costelloe ◽  
Ian C. Roach
Keyword(s):  
South Australia ◽  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Survey

scholarly journals High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data

2019 ◽  
Vol 23 (6) ◽  
pp. 2561-2580 ◽  
Author(s):  
Zhenjiao Jiang ◽  
Dirk Mallants ◽  
Luk Peeters ◽  
Lei Gao ◽  
Camilla Soerensen ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Digital Elevation Model ◽  
South Australia ◽  
Training Dataset ◽  
Valley Bottom ◽  
Archie’S Law ◽  
Digital Elevation ◽  
Airborne Electromagnetic ◽  
Elevation Model

Abstract. Paleovalleys are buried ancient river valleys that often form productive aquifers, especially in the semiarid and arid areas of Australia. Delineating their extent and hydrostratigraphy is however a challenging task in groundwater system characterization. This study developed a methodology based on the deep learning super-resolution convolutional neural network (SRCNN) approach, to convert electrical conductivity (EC) estimates from an airborne electromagnetic (AEM) survey in South Australia to a high-resolution binary paleovalley map. The SRCNN was trained and tested with a synthetic training dataset, where valleys were generated from readily available digital elevation model (DEM) data from the AEM survey area. Electrical conductivities typical of valley sediments were generated by Archie's law, and subsequently blurred by down-sampling and bicubic interpolation to represent noise from the AEM survey, inversion and interpolation. After a model training step, the SRCNN successfully removed such noise, and reclassified the low-resolution, converted unimodal but skewed EC values into a high-resolution paleovalley index following a bimodal distribution. The latter allows us to distinguish valley from non-valley pixels. Furthermore, a realistic spatial connectivity structure of the paleovalley was predicted when compared with borehole lithology logs and a valley bottom flatness indicator. Overall the methodology permitted us to better constrain the three-dimensional paleovalley geometry from AEM images that are becoming more widely available for groundwater prospecting.

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