Matteo Ravasi is an Assistant Professor in the Earth Science and Engineering Division. He is the Principal Investigator of the Deep Imaging Group (DIG) and a member of the Extreme Computing Research Center (ECRC)

Education and early career

Matteo holds a B.Sc. and a M.Sc. in Telecommunication Engineering from Politecnico di Milano and a Ph.D. in Geophysics from the University of Edinburgh. Prior to joining KAUST in 2021, Matteo held a variety of roles in Equinor (nee Statoil) both within research and operations and has led the development of several open- source software products in the geophysical domain, such as segyio and PyLops

Areas of expertise and current scientific interests

Professor Ravasi's research interests are in the area of geophysical inverse problems with applications to seismic acquisition and processing, imaging, quantitative interpretation, and time-lapse monitoring. He is also interested in the use of machine learning and high-performance computing and is heavily involved in the development of open-source software for scientific computing. Within the context of HPC, my research group is currently pushing the boundaries in the development of seismic processing algorithms that are memory and compute intensive and rely on the solution of massive-scale inverse problems. Working alongside other members of ECRC, our research is showing that with the help of SVD-based compression and mixed precision, algorithms that were previously out of reach for 3D datasets can now be implemented at scale on a variety of hardware.

Why Geophysical Inverse Problems?

Jon Claerbout, one of the fathers of geophysical processing, likes to remind people that in the 80’s he was invited give a presentation in the offices of one of the major seismic contractors. His suggestion to give a talk about inverse problems and their potential for seismic processing received a resounding push back; instead he was invited to focus his talk on recent advances in more conventional processing methods. After many years of research, we know that what the geophysicists were eager to listen to at that time are nothing more than the adjoints of forward linear operators that are nowadays used with an inversion framework of many processing algorithms!

Since I was first introduced to inverse problems during my M.Sc. degree, I have always been fascinated by their application to geophysical problems. Combining the laws of physics, principles of linear algebra, and advanced computing we can nowadays image what happens thousands of meters deep by simply using measurements of tiny shakes recorded at the surface of the Earth - it is hard to believe there is something more fascinating than this. And, despite many years of research in the field, we keep finding new and more advanced ways to extract information from such wiggly time series data!


The Middle East is the homeland of al-jabr (Arabic name for Algebra). My passion for Inverse Problems would have not been possible without such early findings. KAUST, with its unique setup that fosters passion driven research, is the perfect place to further progress our scientific knowledge and educate the next generation of scientists. Finally, as it is only through collaboration that great scientific advances can be achieved, ECRC represents a special scientific environment that fosters collaboration between groups with complementary expertise.

Awards and Distinctions

  • SEG J. Clarence Karcher Award, 2018
  • RAS Keith Runcorn Prize for best Ph.D. Thesis in Geophysics, 2015
  • Gustavo Sclocchi Theses Award - Best Ph.D. Thesis, 2015
  • Gustavo Sclocchi Theses Award - Honorable mention for M.Sc. Thesis, 2012

Education Profile

  •  Ph.D. Geophysics, University of Edinburgh, 2015
  • M.S.c Telecommunications, Politecnico di Milano, 2011

Selected Publications

Y Hong, Y., Ltaief, H., Ravasi, M., Gatineau, L., and Keyes, D.E., 2021, Accelerating Seismic Redatuming Using Tile Low-Rank Approximations on NEC SX-Aurora TSUBASA. Supercomputing Frontiers and Innovations.
Ravasi, M. and Vasconcelos., I., 2020, PyLops - A Linear-Operator Python Library for large scale optimization. SoftwareX, 11.
Ravasi, M., 2017, Rayleigh-Marchenko redatuming for target-oriented, true-amplitude imaging. Geophysics, 82(6).
Vasconcelos, I., Ravasi, M., and van der Neut, J., 2017, Subsurface-domain, interferometric objective functions for target-oriented waveform inversion, Geophysics, 82(4)
Ravasi, M., and Curtis, A., 2013, Elastic imaging with exact wavefield extrapolation for application to ocean-bottom 4C seismic data, Geophysics 78(6)