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 …

Ground-penetrating radar (GPR) is the tool of choice for fast near-surface structure and
anomaly imaging. A clean, high-resolution GPR image is crucial to reduce uncertainties in …

Climate projections at fine spatial resolutions are required to conduct accurate risk
assessment for critical infrastructure and design adaptation planning. Generating these …

X-ray micro-computed tomography (micro-CT) has been widely leveraged to characterise
the pore-scale geometry of subsurface porous rocks. Recent developments in super …

In order to develop reliable models, the geoscientific community requires high-resolution
data sets. However, the collection of such data is a persistent challenge due to the …

Due to their cost-effectiveness, aeromagnetic data have been acquired for decades to guide
mineral exploration. Aeromagnetic map enhancement is immensely useful as it allows …

We present the first application of a fast super resolution convolutional neural network
(FSRCNN) based approach for downscaling earth system model (ESM) simulations. Unlike …

Radar sounder (RS) profiles are essential for imaging the subsurface of planetary bodies
and the Earth as they provide valuable geological insights. However, the limited availability …

Recently, deep convolutional neural networks (CNNs) have revolutionized image “super
resolution”(SR), dramatically outperforming past methods for enhancing image resolution …

The past few years have seen an accelerating integration of deep learning (DL) techniques
into various remote sensing (RS) applications, highlighting their power to adapt and …