Frequency extrapolation to enhance the deconvolution of transmitted seismic waves

Dasgupta, Sonali; Nowack, Robert L.

doi:10.1088/1742-2132/5/1/012

Cited by 7 publications

(1 citation statement)

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“…Karslı (2009) applied the autoregressive extrapolation approach to 2D marine data to improve its interpretability. In their studies, Dasgupta and Nowack (2008) investigated the enhanced deconvolution of transmitted seismic waves from distant natural sources using autoregressive extrapolation.…”

Section: Introductionmentioning

confidence: 99%

An application of the autoregressive extrapolation technique to enhance deconvolution results: a 2D marine data example

Karsli

2010

Geophysical Prospecting

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A B S T R A C TInterpreting a post-stack seismic section is difficult due to the band-limited nature of the seismic data even post deconvolution. Deconvolution is a process that is universally applied to extend the bandwidth of seismic data. However, deconvolution falls short of this task as low and high frequencies of the deconvolved data are either still missing or contaminated by noise. In this paper we use the autoregressive extrapolation technique to recover these missing frequencies, using the high signal-to-noise ratio (S/N) portions of the spectrum of deconvolved data.I introduce here an algorithm to extend the bandwidth of deconvolved data. This is achieved via an autoregressive extrapolation technique, which has been widely used to replace missing or corrupted samples of data in signal processing. This method is performed in the spectral domain. The spectral band to be extrapolated using autoregressive prediction filters is first selected from the part of the spectrum that has a high signal-to-noise ratio (S/N) and is then extended. As there can be more than one zone of good S/N in the spectrum, the results of prediction filter design and extrapolation from three different bands are averaged.When the spectrum of deconvolved data is extended in this way, the results show higher vertical resolution to a degree that the final seismic data closely resemble what is considered to be a reflectivity sequence of the layered medium. This helps to obtain acoustic impedance with inversion by stable integration. The results show that autoregressive spectral extrapolation highly increases vertical resolution and improves horizon tracking to determine continuities and faults. This increase in coherence ultimately yields a more interpretable seismic section.

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Section: Introductionmentioning

confidence: 99%