“Deep learning is ubiquitous in data processing. The question is whether we have the courage to change the way we work.”

Yangkang Chen discusses how deep learning has moved from experimentation to production in seismic processing and earthquake monitoring. Drawing on a decade-long effort to build an operational AI-driven monitoring system, he explains why tasks like first-arrival picking, velocity analysis, denoising, and reconstruction are especially well-suited for deep learning. Yangkang emphasizes that success depends not just on algorithms, but on benchmarks, stability, teamwork, and trust. He also highlights how open and reproducible research lowers barriers for adoption and helps geophysicists apply AI confidently in real workflows.

Key Takeaways

• Deep learning excels at repetitive, label-intensive seismic tasks that are slow and inconsistent using traditional methods.
• Operational AI requires trust, built through benchmarks, validation, and a clear understanding of model behavior.
• Open and reproducible workflows accelerate adoption, collaboration, and innovation across the geophysics community.

Links

• Register for his course, Deep learning for revolutionizing seismic data processing, on March 24-25, 2026.
• Watch his webinar, Key Factors in Making a Revolutionary Change in Passive Seismic Monitoring Using AI

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Show Credits

Andrew Geary at TreasureMint hosted, edited, and produced this episode. The SEG podcast team comprises Robin Dupre, Kathy Gamble, and Ally McGinnis. 

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