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Title: The discrete Kalman filtering approach for seismic signals deconvolution

Abstract

Seismic signals are a convolution of reflectivity and seismic wavelet. One of the most important stages in seismic data processing is deconvolution process; the process of deconvolution is inverse filters based on Wiener filter theory. This theory is limited by certain modelling assumptions, which may not always valid. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The main advantage of Kalman filtering is capability of technique to handling continually time varying models and has high resolution capabilities. In this work, we use discrete Kalman filter that it was combined with primitive deconvolution. Filtering process works on reflectivity function, hence the work flow of filtering is started with primitive deconvolution using inverse of wavelet. The seismic signals then are obtained by convoluting of filtered reflectivity function with energy waveform which is referred to as the seismic wavelet. The higher frequency of wavelet gives smaller wave length, the graphs of these results are presented.

Authors:
;  [1]
  1. Departement of Physics Intitut Teknologi Bandung, Jl. Ganesha 10 Bandung (Indonesia)
Publication Date:
OSTI Identifier:
22068979
Resource Type:
Journal Article
Journal Name:
AIP Conference Proceedings
Additional Journal Information:
Journal Volume: 1454; Journal Issue: 1; Conference: ICPAP 2011: International conference on physics and its applications, Bandung (Indonesia), 10-11 Nov 2011; Other Information: (c) 2012 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-243X
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; 58 GEOSCIENCES; COMPUTERIZED SIMULATION; DATA PROCESSING; MATHEMATICAL MODELS; REFLECTIVITY; RESOLUTION; SEISMIC WAVES; SIGNALS; WAVE FORMS; WAVELENGTHS

Citation Formats

Kurniadi, Rizal, and Nurhandoko, Bagus Endar B. The discrete Kalman filtering approach for seismic signals deconvolution. United States: N. p., 2012. Web. doi:10.1063/1.4730695.
Kurniadi, Rizal, & Nurhandoko, Bagus Endar B. The discrete Kalman filtering approach for seismic signals deconvolution. United States. doi:10.1063/1.4730695.
Kurniadi, Rizal, and Nurhandoko, Bagus Endar B. Wed . "The discrete Kalman filtering approach for seismic signals deconvolution". United States. doi:10.1063/1.4730695.
@article{osti_22068979,
title = {The discrete Kalman filtering approach for seismic signals deconvolution},
author = {Kurniadi, Rizal and Nurhandoko, Bagus Endar B.},
abstractNote = {Seismic signals are a convolution of reflectivity and seismic wavelet. One of the most important stages in seismic data processing is deconvolution process; the process of deconvolution is inverse filters based on Wiener filter theory. This theory is limited by certain modelling assumptions, which may not always valid. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The main advantage of Kalman filtering is capability of technique to handling continually time varying models and has high resolution capabilities. In this work, we use discrete Kalman filter that it was combined with primitive deconvolution. Filtering process works on reflectivity function, hence the work flow of filtering is started with primitive deconvolution using inverse of wavelet. The seismic signals then are obtained by convoluting of filtered reflectivity function with energy waveform which is referred to as the seismic wavelet. The higher frequency of wavelet gives smaller wave length, the graphs of these results are presented.},
doi = {10.1063/1.4730695},
journal = {AIP Conference Proceedings},
issn = {0094-243X},
number = 1,
volume = 1454,
place = {United States},
year = {2012},
month = {6}
}