Events

Speaker: Hongyu Sun, Postdoctoral Scholar in Geophysics, California Institute of Technology

Host: Benjamin Keisling

Title: Next-Generation Seismic Monitoring and Imaging with Artificial Intelligence

Abstract: Numerous natural hazards, such as earthquakes, volcanic activities, and landslides, are sources of seismic waves. Seismology allows us to understand and reduce the risks of these hazards by investigating the origins of the seismic waves and inferring the structure and properties of the Earth’s interior. AI has transformed seismic data analysis, elevating the role of deep learning in seismology. In this talk, I will outline my contributions to improving seismic monitoring and subsurface imaging with AI. I will first present the Phase Neural Operator (PhaseNO) for earthquake detection and seismic phase picking. PhaseNO measures the arrival times of P- and S-waves from continuous seismic data simultaneously across input stations with arbitrary geometries. By leveraging the spatial-temporal information, PhaseNO outperforms single-station AI algorithms by detecting significantly more earthquakes and enhancing measurement accuracy. Additionally, I will show how deep neural networks can overcome the complexities in seismic imaging by being trained to generate seismic waves. These waves, although not directly recorded, are essential for imaging the Earth’s interior. I will provide case studies on full-waveform inversion with active-source seismic data and seismic interferometry with environmental noise. In summary, these AI methods are powerful complements to traditional computational methods and hold significant promise for mitigating natural hazards and climate change.