Noise whitening of seismic data

C. Birnie*, K. Chambers, D. Angus

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Noise is a persistent feature in seismic data and poses a particular challenge for microseismic monitoring where events are often at or below the noise level of individual recordings. This work introduces a statistics-driven noise suppression technique that whitens noise through the calculation and removal of the noise's covariance. Using the Aquistore CO2 storage site as an example, the technique is shown to reduce the noise energy by a factor of 3.5 whilst having negligible effect on the seismic wavelet. This opens up the opportunity to identify and image events below current detection levels. Furthermore, while the technique has been discussed with respect to a microseismic monitoring application, it is applicable to any situation where noise may be considered as a multivariate Gaussian distribution, including other exploration, global and hazard monitoring scenarios.

Original languageEnglish (US)
Title of host publication79th EAGE Conference and Exhibition 2017
PublisherEuropean Association of Geoscientists and Engineers, EAGE
ISBN (Electronic)9789462822177
StatePublished - 2017
Event79th EAGE Conference and Exhibition 2017: Energy, Technology, Sustainability - Time to Open a New Chapter - Paris, France
Duration: Jun 12 2017Jun 15 2017

Publication series

Name79th EAGE Conference and Exhibition 2017


Conference79th EAGE Conference and Exhibition 2017: Energy, Technology, Sustainability - Time to Open a New Chapter

Bibliographical note

Funding Information:
The authors would like to thank the Petroleum Technology Research Centre (PTRC) for access to Aqui-store Data. C. Birnie is funded by the NERC Open CASE studentship NE/L009226/1 and Pinnacle-Halliburton. D. Angus acknowledges the Research Council UK (EP/K035878/1; EP/K021869/1; NE/L000423/1) for financial support.

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics


Dive into the research topics of 'Noise whitening of seismic data'. Together they form a unique fingerprint.

Cite this