Earth signal analysis using AI engineering, for optimal and sustainable energy resource and environmental management.
Applying machine learning to seismic interpretation and subsurface signal analysis — automating fault detection, facies classification, and horizon mapping.
Well correlation, fault interpretation, seismic horizon mapping, and petrophysical analysis, grounded in hands-on fieldwork.
Building computational tools in Python to extract, process, and interpret signals from subsurface data.
Growing up in Nigeria, irregular power supply has always been the norm, and it has hindered productivity a great deal.
I'm a geophysicist, and I get into flow when I'm playing with signals and working with them to make sense of what they reveal. Geophysics gives me that opportunity, interpreting what lies kilometres beneath the surface.
Nigeria sits on untapped gas reservoirs large enough to transform its energy landscape, yet gas continues to be flared because capturing it isn't considered economically viable. That realisation is part of why I want to join hands with professionals to develop sustainable ways to tap into our resources while being responsible for the environment.
My core focus is subsurface geophysics: understanding what lies beneath the surface, and applying Python and machine learning to interpret it faster and with greater confidence.
Full petrophysical analysis using Python scripts to derive porosity and fluid saturation across multiple wells for economic field valuation.
Applying machine learning to open seismic datasets for fault detection and facies classification.
If you're working on subsurface signal analysis, machine learning research, or graduate research opportunities in geophysics and AI, I want to hear from you.
Open to research collaborations and graduate research opportunities.