Christof Stork

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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 theory alone is not enough. 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 he can get his hands on more land data.  

Christof Stork

2024 SEG Distinguished Lecturer

How Does the Thin Near Surface of the Earth Produce up to 100 Times More Noise on Land Seismic Data than on Marine Data?

Land seismic data is often dramatically noisier than marine data. Processed images and attributes from land data can be severely degraded by this noise. The acquisition effort needed to address the noise is often the biggest cost factor and the biggest uncertainty with land seismic data. The physics behind land seismic noise is fascinating. Much of the noise is caused by the very near surface, less than 30m, which often has ultra slow velocities. This land seismic noise is often complex and irregular — much more so than simple surface waves. Even simple scattering models are insufficient. We give a physical intuition of this noise generation through wave propagation movies and synthetic data examples with realistic models. Better understanding the physics can lead to better appreciation and customization of the acquisition and processing for the noise types.  

We propose the following conclusions for many areas:

  1. Micro-near surface effects are important
  2. Surface distortion of signal is more complex than the statics, SCamps, and SC-Decon model and this can be strong
  3. You probably have more signal in your data than you think
  4. Statistical noise removal has limits
  5. There is much value in coherent noise removal
  6. There is limited benefit to dense inline receiver spacing
  7. Aa better use of high channel counts may be an irregular layout
  8. Noise character may limit CSI-reconstruction approaches
  9. Changes in surface moisture content can produce strong 4D noise for CCUS monitoring
  10. FWI has limited benefit in addressing the surface noise (but it has other good uses)
  11. Specialized modeling of the noise in an area and testing methods to address it can be helpful.