Los Alamos National Laboratory
Youzuo Lin is a staff scientist in the Geophysics Group at Los Alamos National Laboratory. His research interests lie in computation and machine learning methods and their applications in various subsurface problems. Youzuo received his Ph.D. in Applied and Computational Mathematics from Arizona State University in 2010. After completing his Ph.D., he was a postdoctoral fellow in the Geophysics Group at Los Alamos National Laboratory from 2010 to 2014, and then converted as a staff scientist.
Seismic full-waveform inversion is a typical non-linear and ill-posed large-scale inverse problem. It is an important and widely used geophysical exploration method to obtain subsurface structures. Existing physics-driven computational methods for solving waveform inversion usually suffer from the cycle skipping and local minima issues, and not to mention that solving waveform inversion is computationally expensive. We recently developed several data-driven inversion techniques to reconstruct subsurface structures. Our data-driven inversion approaches are end-to-end frameworks that can generate high-quality subsurface structure images directly from the raw seismic waveform data. A series of numerical experiments are conducted on the synthetic seismic reflection data to evaluate the effectiveness of our methods. In this talk, I will discuss the pros and cons of physics-driven and data-driven inversion techniques. Particularly, I will compare the accuracy of the reconstruction as well as computational efficacy. Furthermore, I will also discuss the possibility of combining both types of methods with the hope of benefiting each other.