Speaker

Xu Yang got his Ph.D. at the University of Wisconsin-Madison in 2008, and spent two years at Princeton and two years at Courant Institute of NYU as a postdoc. He joined the University of California, Santa Barbara as an assistant professor in 2012, and became a full professor in 2020. His current research focuses on seismic imaging using realistic earthquake data. He has also been working on the applied analysis and numerical computation of scientific problems, including photonic graphene, ferromagnetic materials, and biological modeling.

Abstract

The stochastic gradient descent (SGD) method and deep neural networks (DNN) are two main workhorses in machine learning. In this talk, we present some preliminary results on connecting SGD and DNN to the applications in seismic tomography. On the one hand, motivated by SGD, we propose to use random batch methods to construct the gradient for iterations in seismic tomography. On the other hand, we use deep neural networks to create a reliable PmP database from massive seismic data and study the case in Southern California. The major difficulty lies in that the identifiable PmP waves are rare, making the problem of identifying the PmP waves from a massive seismic database inherently unbalanced.