Episode 224: The Physics Behind Land Seismic Noise (Christof Stork)

“With ​every ​new ​acquisition, ​spend ​2% ​of ​your ​budget ​to ​do ​research ​to ​understand the ​noise ​and ​signal ​drivers ​in ​your ​area. ​It ​might ​help ​any ​future ​neighboring ​acquisition, ​or ​it ​might ​just ​help ​the ​field ​in ​general, ​​which ​you ​and ​your ​career ​will ​benefit ​from.”

Dr. Christof Stork discusses his Distinguished Lecture, “How Does the Thin Near Surface of the Earth Produce up to 100 Times More Noise on Land Seismic Data than on Marine Data?”.


  • The significance of addressing noise in near-surface land seismic data
  • The interplay of theoretical and practical approaches in seismic noise reduction
  • The importance of coherent noise removal before statistical noise removal
  • The limitations and future potential of FWI in land seismic
  • The role of research and development in improving land seismic data quality
  • The impact of corporate and economic structures on seismic research investment
Seismic Soundoff · 224: The Physics Behind Land Seismic Noise (Christof Stork)

Along with host Andrew Geary, Christof delves into the intricate world of land seismic noise. Noise is a significant challenge in land seismic data that has long been overlooked. Christof focuses on the complexities of land seismic noise and innovative methods to address it.

Christof’s journey into addressing land seismic noise began with his fascination for land data and its significant yet under-researched impact on seismic imaging. Despite the advancements in marine processing and pre-stack depth migration, land seismic methods have yet to see comparable progress. He emphasizes the need for more attention and funding in this area, highlighting how crucial it is for improving seismic data quality.

Christof also illuminates the concept of “poor coupling,” a term that describes the disconnect between surface geophones and the wave field at greater depths. He argues that understanding the physics behind poor coupling can lead to better signal extraction and improved seismic data quality.

One key takeaway from Christof’s discussion is the importance of combining theoretical and practical approaches to tackle land seismic noise. He explains that while traditional methods rely heavily on statistical noise removal, this is limited in effectiveness. Instead, Christof advocates for a focus on coherent noise removal, which can significantly enhance the effectiveness of statistical methods and reduce the need for excessive data collection.

Christof’s passion for land seismic research is evident as he calls for more investment in R&D from both the industry and academic communities. He stresses the potential benefits of dedicating a small percentage of acquisition budgets to research and creative experimentation, which could lead to significant advancements in seismic imaging.

Listeners will better understand why land seismic noise remains a complex problem and the potential pathways to progress. 

Episode Links

Guest Bios

Christof Stork started as a theoretical academic with a PhD in geophysics from Caltech and a post-doc from Stanford 36 years ago. He performed early leading work in reflection tomography, PSDM, WEM, RTM, and FWI before they became mainstream technologies. Ten years ago, he decided to take on noisy land seismic data where more than theory is needed. Christof has been involved with four startup companies in his quest to avoid Houston and make theory commercially viable. He’s now on his fifth, last, and craziest startup company, a land seismic processing company so that he can get his hands on more land data.

Show Credits

Seismic Soundoff showcases conversations addressing the challenges of energy, water, and climate.

SEG creates these episodes to celebrate and inspire the geophysicists of today and tomorrow.

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

Transcription and episode summary support provided by Headliner.

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