Fiber Optics, DAS, and the Future of Geophysical Monitoring

"Sometimes the traditional methods are way more effective or efficient in handling certain things. To succeed in this new paradigm, we need to build on our strong fundamentals and progress further." Ivan Lim Chen Ning shares how data-driven methods are reshaping geophysics by challenging traditional workflows and opening new possibilities. He highlights the role of AI, machine learning, and fiber-optic sensing in improving seismic interpretation, imaging, and monitoring. His insights show how combining strong fundamentals with modern digital tools can help geophysicists solve problems more effectively. Read the September issue of TLE about data-driven geophysics at https://library.seg.org/toc/leedff/44/9. KEY TAKEAWAYS > AI and data-driven tools open new paths. They help geophysicists move beyond traditional workflows to find faster and simpler solutions. > Fiber-optic sensing changes monitoring. DAS provides continuous well data, replacing point sensors and revealing signals directly. > Strong fundamentals still matter. Success comes from combining proven geophysical methods with modern digital skills. GUEST BIO Ivan Lim Chen Ning is an Earth Scientist – Fiber Optics at Chevron, where he analyzes Distributed Fiber Optic Sensing (DFOS) data and develops real-time algorithms for field applications. He applies deep learning and signal processing to improve DFOS workflows, advancing distributed acoustic sensing in the energy industry. A member of Chevron’s Emerging Leader 2024 cohort, Ivan is recognized for solving cross-disciplinary challenges and driving innovation to help secure energy for the future.