Widely linear denoising of multicomponent seismic data
B. Bahia and M.D. Sacchi
Journal name: Geophysical Prospecting
Issue: Vol 68, No 2, February 2020 pp. 431 - 445
Info: Article, PDF ( 7.99Mb )
Seismic data processing is a challenging task, especially when dealing with vectorvalued datasets. These data are characterized by correlated components, where different levels of uncorrelated random noise corrupt each one of the components. Mitigating such noise while preserving the signal of interest is a primary goal in the seismic-processing workflow. The frequency-space deconvolution is a well-known linear prediction technique, which is commonly used for random noise suppression. This paper represents vector-field seismic data through quaternion arrays and shows how to mitigate random noise by proposing the extension of the frequency-space deconvolution to its hypercomplex version, the quaternion frequency-space deconvolution. It also shows how a widely linear prediction model exploits the correlation between data components of improper signals. The widely linear scheme, named widely-linear quaternion frequency-space deconvolution, produces longer prediction filters, which have enhanced signal preservation capabilities shown through synthetic and field vector-valued data examples.