On the Application of the Fast Kalman Algorithm to Adaptive Deconvolution of Seismic Data

Mahalanabis, A. K.; Prasad, S.; Mohandas, K. P.

doi:10.1109/tgrs.1983.350503

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…Both deconvolutions have to fulfill the stationary and the noise-free wavelet assumptions but not the random reflectivity and the minimum phase ones (Arya and Holden, 1978). Crump (1974) designed the Kalman Filter matrixes for deconvolution, and later, Mahalanabis et al (1983) improved the storage and updating of the matrix by estimating both the smoothed forward and backward prediction residuals of the trace, turning the algorithm computationally more efficient. Despite the above, the high computational cost remains.…”

Section: Introductionmentioning

confidence: 99%

Extended Kalman Filter deconvolution for extracting accurate seismic reflectivity

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2022

Boletín de Geología

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Deconvolution attempts compensating for the distortions affecting a recorded seismogram, increasing its bandwidth and extracting subsurface reflectivity from such seismic trace. The estimated reflectivity needs the highest reliability and resolution because of its subsequent use in the pre-stack seismic processing sequence and seismic inversion. We implemented the predictive deconvolution algorithms, the homomorphic Phase Inversion, and the Extended Kalman Filtering. Their application to synthetic traces extracted reflectivity whose comparison with well-bore allowed comparing the reliability between methods. The algorithms applied to an offshore record provided results whose comparison permitted to analyze the impact of the deconvolution assumptions on each method performance.

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Section: Introductionmentioning

confidence: 99%

Extended Kalman Filter deconvolution for extracting accurate seismic reflectivity

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Almanza

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2022

Boletín de Geología

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“…For instance, Crump (1974) first uses the discrete Kalman filter successfully for the deconvolution of the seismic signals to generate an estimate of the reflectivity function. Mahalanabis et al (1981Mahalanabis et al ( , 1983 propose fast and adaptive Kalman filter algorithms. Using the Kalman filter with different dimensions applied to seismic traces, Sayman (1992) uses optimal fixed-interval smoothing filters to attenuate the unexpected high-frequency energy caused by the original Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Seismic data deconvolution using Kalman filter based on a new system model

2016

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Seismic resolution plays an important role in geologic interpretation and reservoir prediction. To improve the vertical resolution of a seismic image, we have developed a new Kalman filter system model for seismic deconvolution. Similar to the conventional Kalman filter model for seismic deconvolution, our new Kalman model is also based on the common viewpoint that a reflected seismic record can be regarded as a convolution of a seismic wavelet with a reflection coefficient series. The new model uses a reversed seismic wavelet to slide across a reflectivity function to achieve the convolution result, instead of using a reversed reflectivity function to slide across a seismic wavelet in the conventional Kalman filter model. A simpler state equation for the new model is achieved, and the number of parameters to select is fewer than the conventional. Furthermore, the number of parameters can be reduced to only one by a theoretical demonstration for stationary noisy signals, which decreases the requirement for multiple parameters selection in the conventional model. The practical selection for this parameter should be a compromise between resolution improvement and noise amplification. Experimental results in the time and frequency domains on synthetic and field seismic records revealed that the Kalman filter based on the new model has the advantages of a higher resolution and peak signal-to-noise ratio (PS/N) than the conventional Kalman filter for stationary and nonstationary signals, and it works similarly to the Wiener filter for stationary signals, and it is superior to the Wiener filter in resolution and PS/N for nonstationary signals. The Kalman filter based on the new model can be applied to seismic resolution improvement.

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“…?at recent interest and a number of efforts have been rt!ported which apply the modern estimation theory techniques to improve the performance over the classically used, simple predictive deconvolution techniques. Amongst some of the notable recent approaches to this problem for seismic applications are the "white-noise" B~rnoulli sequence estimationfdetection approach of Mendel and his co-workers [l-4] (see also [S]) and the adaptive predictive deconvolution approaches of Griffiths [6], Prasad and Mahalanabis [7] and their co-workers [8][9][10]. The state-variable formulation of the deconvolution problem (also called the input white-noise estimation problem) and its several solutions suggested in [I-S] are very elegant and should be very useful whenever sufficient information is available about the nature of the seismic wavelet to warrant such modelling.…”

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confidence: 99%

“…The state-variable formulation of the deconvolution problem (also called the input white-noise estimation problem) and its several solutions suggested in [I-S] are very elegant and should be very useful whenever sufficient information is available about the nature of the seismic wavelet to warrant such modelling. The adaptive, tim~-varying op!rators either in the tapped-delay line or the !attic~ filter forms suggested in [6][7][8][9][10], on the other hand, are likely to prove effective on nonstationary seismic data as from offshore experiments.…”

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confidence: 99%