Paul Goyes-Peñafiel

PhD Candidate in Computer Science, Universidad Industrial de Santander

Geophysics combines physics, mathematics, and computational methods to study the Earth’s subsurface using indirect measurements such as seismic, gravimetric, and electromagnetic data. It plays a crucial role in energy exploration, natural hazard assessment, and environmental monitoring, where direct observation of the subsurface is not possible. Inverse theory and deep learning have become essential tools in this field, allowing researchers to reconstruct accurate subsurface models from incomplete or noisy data.

About me

I am a geologist and geophysicist working at the intersection of inverse theory, compressive sensing, and deep learning applied to geophysical exploration. I hold a Bachelor’s degree in Geology from Universidad Industrial de Santander (UIS) and a Master’s degree in Geology/Geophysics from Perm State University (Russia). I am currently completing my PhD in Computer Science at UIS, where my research focuses on optimizing 3D survey geometries and recovery algorithms for compressive seismic acquisition. My work has been published in journals including IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, Leading Edge, and Gondwana Research. In 2023, I completed a research internship at the Computational Imaging Group at Washington University in St. Louis, focused on diffusion models for seismic data reconstruction. I currently teach and conduct research at UIS, working on multi-physics inversion for geothermal exploration and deep learning applications in geophysics.