TY - GEN
T1 - Noise whitening of seismic data
AU - Birnie, C.
AU - Chambers, K.
AU - Angus, D.
N1 - 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.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85088204370&partnerID=8YFLogxK
U2 - 10.3997/2214-4609.201701061
DO - 10.3997/2214-4609.201701061
M3 - Conference contribution
AN - SCOPUS:85088204370
T3 - 79th EAGE Conference and Exhibition 2017
BT - 79th EAGE Conference and Exhibition 2017
PB - European Association of Geoscientists and Engineers, EAGE
T2 - 79th EAGE Conference and Exhibition 2017: Energy, Technology, Sustainability - Time to Open a New Chapter
Y2 - 12 June 2017 through 15 June 2017
ER -