Mahmud Mukhtar

Research

The Earth, being made up of different materials and complex structures is a heterogeneous medium. This medium, possessing different mineral constituents, undergoes numerous chemical and physical changes. Understanding the internal structure of the Earth is therefore crucial in accessing natural hazards and importantly, in making geophysical explorations related to geothermal harnessing and critical mineral mining -both of which requires precise images of the Earth’s subsurface.
 
Obtaining these images requires understanding Earth’s heterogeneities effects on waves propagation – a process known as inversion. However, the inversion process is inherently ill-posed as multiple subsurface models can explain the same wave propagation data. As a result, the images produced are typically a blurred representation of the reality, lacking fine structural resolution needed for precise geophysical exploration.

My project focuses on exploring models that provide more physically accurate and stably reconstructed images by accounting for effective impact of sub-resolution features – a process known as tomographic upscaling.  To enhance the quality of images obtained from geophysical inversion, I will explore a range of techniques, including homogenization theory, super-resolution, physics informed neural networks, and other machine learning approaches.