"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.
How Distributed Chemical Sensing Could Rewrite the Rules of Risk Management
Seismic Soundoff
26 minutes 46 seconds
2 months ago
How Distributed Chemical Sensing Could Rewrite the Rules of Risk Management
“Distributed chemical sensing really is the next frontier in fiber-optic monitoring. It adds a new dimension by directly connecting chemical changes in the environment to signals we can read.”
Distributed chemical sensing (DCS) is an emerging technology that utilizes fiber optics to measure chemical changes over long distances in real-time. Authors Christian Totland, Thomas Dylan Mikesell, and Peter James Thomas join host Andrew Geary to discuss their new paper, "Distributed chemical sensing: An unexplored frontier in urban, industrial, and environmental monitoring." Unlike traditional point sensors, which only capture data at one location, DCS has the potential to provide continuous chemical information with both high spatial and temporal resolution. This innovation could transform how we monitor pipelines, groundwater, and industrial sites, while also opening new opportunities for collaboration between geophysics, chemistry, and material science.
KEY TAKEAWAYS
> DCS can detect leaks, contamination, and chemical changes directly, offering more accurate monitoring than pressure or temperature proxies.
> The technology is still in its early stages, which means there are many opportunities for research, innovation, and interdisciplinary collaboration.
> If developed further, DCS could provide affordable, real-time monitoring for critical infrastructure and environmental systems worldwide.
LINKS
* Christian Totland, Thomas Dylan Mikesell, and Peter James Thomas, (2025), "Distributed chemical sensing: An unexplored frontier in urban, industrial, and environmental monitoring," The Leading Edge 44: 598–605. - https://doi.org/10.1190/tle44080598.1
* Learn more about this special section on urban and infrastructure geophysics - https://library.seg.org/doi/10.1190/tle44080587.1
* Listen to Haipeng Li's interview (also from this special section) - https://seg.org/podcasts/episode-271-the-low-cost-seismic-revolution-already-buried-in-your-city/
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.
