Event Description
You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.
Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.
Machine Learning for Seismic Low Frequency Extrapolation
Abstract: The cycle skipping problem that plagues seismic inversion can be mitigated by utilizing low-frequency seismic data, which captures the kinematics of wave propagation, in conjunction with a reasonable initial velocity model. However, seismic sources and receivers are band-limited and cannot provide signals down to 0 Hz. To improve solution of the seismic inverse problem one can synthesize the missing low-frequency content by solving a regression problem using machine learning (ML). The recorded high-frequency (HF) seismic data is the input and the ML models are trained to predict the missing low-frequency (LF) seismic data. Deep learning models utilizing convolutional neural networks (CNNs) and generative adversarial networks (GANs) demonstrate important capabilities for LF extrapolation. However, such models require powerful hardware and careful training. We explore the feasibility of using less costly ML models such as a random forest, Gaussian process surrogates, and gradient boosting as alternatives to computationally expensive deep learning models.
Biography: Sue Minkoff is Chair of Applied Mathematics at Brookhaven National Laboratory. From 2012-2024 she was a Professor of Mathematical Sciences and an Affiliated Professor in the Departments of Sustainable Earth Systems Sciences and Science and Mathematics Education at the University of Texas at Dallas. From 2000-2012 she served on the faculty in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County. She received her doctorate in Computational and Applied Mathematics from Rice University. From 1995-1997 she was a National Science Foundation-Industrial postdoc joint with the University of Texas at Austin and British Petroleum, and from 1997-2000 she held the von Neumann Fellowship in the Mathematics Department at Sandia National Labs. In 2000 Minkoff was promoted to Senior Member of the Technical Staff in Sandia's Geophysics Department. Minkoff's research interests include scientific computing, inverse problems, uncertainty quantification and digital twins modeling, Earth science, and photonics.
Location: CDS, Bldg. 725, Training Room
Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1606848158?pwd=miUtq7OkYL5SNkjbgVb19teZPNennd.1
Meeting ID: 160 684 8158
Passcode: 068399
