Matteo is an Assistant Professor at KAUST and leads the Deep Imaging Group in the faculty of Earth Science and Engineering. Prior to that, Matteo worked in Equinor both within research and operations. He holds a Phd in Geophysics from the University of Edinburgh and an MSc and BSc in Telecommunication Engineering from Politecnico di Milano.
Matteo has made several contributions in the areas of seismic processing and imaging and inversion. For his work, Matteo is the recipient of the SEG Karcher Award, RAS Keith Runcorn Prize, and Gustavo Sclocchi Theses Award. He is also the inventor of 2 international patents and author of 17 peer reviewed papers. Matteo is heavily involved in the development of several open-source Python libraries such as PyLops and spgl1.
Can we learn robust latent representations directly from seismic data, which can then act as natural priors at various processing stages? In this talk we will report on our initial results showing the superior performance of NN-based representations against ad-hoc transforms (e.g., fk, curvelets) for tasks such as joint data reconstruction and receiver deghosting. We will however also show that this superiority is not magic and is only achieved by carefully choosing the network architecture and hyperparameters.
Chairs: Lukas Mosser and Matteo Ravasi