Magnetic grid resolution enhancement using machine learning: A case study from the Eastern Goldfields Superterrane

Abstract

Densely sampled geophysical surveys are a key driver for mineral exploration, but sample density, and therefore grid resolution, is limited by survey cost. Consequently, computational methods are resorted to for upsampling, or ‘super-resolving’, of gridded geophysical survey data. However, existing approaches such as interpolation filters do not leverage high-resolution detail from pre-existing geophysical surveys. Through the application of state-of-the-art deep learning super-resolution architectures, accurate high-resolution grids are predicted from corresponding low-resolution grid priors. Specifically, the RDN and ESRGAN+ neural network architectures, which were originally developed for enhancing images, are applied to enhance magnetic surveys and trained with high-resolution and low-resolution magnetic grids of the same extent. 80 m cell size magnetic grids are upsampled to 20 m cell size using this method, and predicted value and structural accuracy comparisons to the corresponding ground truth 20 m grids are presented over two test sites in the Eastern Goldfields Superterrane, Western Australia. The method based on RDN achieves 53 % lower error compared to Bicubic interpolation. The case study demonstrates that the deep learning approach can improve the resolution and frequency content of geophysical surveys without requiring additional sampling expense, while remaining accurate against known ground truth surveys. Super-resolution may assist in the interpretation of low-resolution survey grids, however these upsampled grids cannot perfectly recreate the accuracy of highly sampled surveys gridded at their optimal cell size.