Characterization and modeling of structured noise in seismic reflection data

Christian Lundmand Jensen

AbstractObtaining reliable information about the noise in seismic reflection data can be difficult. Noise is often estimated from "expert knowledge" or other available information on which uncertainty is hard to quantify. I propose a method to characterize the noise directly from the data, without the need for external information, by using 4-D data. It is shown, that the covariance of the residual of two 3-D data sets (together constituting 4-D) with identical geological subsurface is a good representation of the noise in the summed data set. The noise is characterized in the form of a positive definite semi-variogram model, and hence can be used in any probabilistic inversion. The method is demonstrated on both synthetic and real data by solving the linear least-squares problem (LSQ, Tarantola [2005]) without change of parameters, effectively removing the modeled noise from the data. Tests on the synthetic data with realistic noise levels show almost perfect removal of additive noise. A real 4-D dataset from the Halfdan field in the North Sea with severe acquisition striping was also de-noised in this way. The data were mild- to moderately non-Gaussian, but the correlated noise was still convincingly attenuated. It is expected, that many commercial inversion algorithms use some form of linearized LSQ. Hence, the method developed here will easily integrate into existing software.
TypeMaster's thesis [Academic thesis]
Year2013
PublisherTechnical University of Denmark, Department of Applied Mathematics and Computer Science / DTU Co
AddressMatematiktorvet, Building 303B, DK-2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk
SeriesM.Sc.-2013-82
NoteDTU supervisor: Klaus Mosegaard, and Thomas Mejer Hansen, DTU Compute
Electronic version(s)[pdf]
Publication linkhttp://www.compute.dtu.dk/English.aspx
BibTeX data [bibtex]
IMM Group(s)Scientific Computing