Scientist & Team Lead for Sensors & Signatures Team @ Los Alamos National Lab, working on Scientific AI, Computational Imaging, Geophysics, and Seismology.
Please check out our recent collaborative work in JGR (co-supervised with Dr. Yuozuo Lin @YouzuoLin1). We develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes.
https://t.co/BLM8d4ZVO6
Very excited to share our OpenFWI was accepted by #NeurIPS. Great job Chengyuan, Shihang, and everyone contributing to this effort!
OpenFWI: https://t.co/5KLCXyE7bQ
I am extremely happy to share that the #ICML2022 submission by us has been accepted for a short presentation.
We show an interesting near-linear relationship after applying the integral transform.
Great job, Yinan & the team!
Here is our work: https://t.co/1cdMngHawJ
It wasn't dinosaurs' best day ever. Watch LANL scientist Cathy Plesko talk about #asteroid impacts and how to deflect them on the "Dinosaur Apocalypse" episode of NOVA on @PBS on May 11. https://t.co/7r6uxTXWE7
@Tieyuan_Zhu Thanks for the reply! 😀 Like what you said, the quantity and quality of the training set are equally important. Our method learns the physics knowledge (i.e. physics perception) and uses it to make sure the synthetic velocity maps generated would be physically meaningful.
You don't have sufficient seismic velocity maps to train your data-driven inversion models?
Hey, our method turns natural images into velocity maps. Check it out below:
Paper: https://t.co/14sgmgKEtd
Look forward to the upcoming SEG Workshop on Data Analytics and Machine Learning for Exploration and Production! Here is the agenda to download: https://t.co/xBRwUFebJW
Excited to share our ICLR work "Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential in a Loop", as a poster presentation this year! Nice job, Peng and Xitong! 👍
Paper: https://t.co/tq0LIiz7fn