"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.
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"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.
The Low-Cost Seismic Revolution Already Buried in Your City
Seismic Soundoff
20 minutes 27 seconds
2 months ago
The Low-Cost Seismic Revolution Already Buried in Your City
"By mapping fiber optic cables accurately, we can transform them into dense seismic arrays. This opens the door to city-scale imaging and monitoring."
Haipeng Li explains how distributed acoustic sensing (DAS) can turn existing urban fiber optic cables into powerful seismic arrays for near-surface imaging and monitoring. By using everyday traffic and ambient noise, his team can track groundwater changes, detect geohazards, and study fault structures without costly field deployments. This approach offers a new way for geophysicists, engineers, and city planners to work together for safer, more resilient urban environments.
KEY TAKEAWAYS
> Existing fiber optic cables can be transformed into dense, city-scale seismic arrays using DAS technology.
> Vehicle-induced seismic waves provide highly repeatable data for monitoring small subsurface changes, while ambient noise helps extend imaging depth.
> Urban DAS monitoring can reveal how infrastructure affects groundwater recharge and can support hazard assessment and infrastructure planning.
GUEST BIO
Haipeng Li is a third-year Ph.D. candidate in the Geophysics Department at Stanford University, advised by Biondo Biondi in the Stanford Earth Imaging Project (SEP). His research centers on spatiotemporal subsurface monitoring, with a focus on developing efficient and robust time-lapse seismic waveform inversion methods and uncertainty quantification approaches. He applies these techniques to real-world challenges such as geological CO2 sequestration for the energy transition and groundwater monitoring in urban environments, often leveraging Distributed Acoustic Sensing (DAS) data. He is a student member of the AGU, SSA, and SEG.
LINKS
* Haipeng Li, Jingxiao Liu, and Biondo L. Biondi, (2025), "Near-surface imaging and monitoring enabled by urban distributed acoustic sensing seismic arrays," The Leading Edge 44: 588–597. - https://doi.org/10.1190/tle44080588.1
Seismic Soundoff
"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.