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Warianty tytułu
Języki publikacji
Abstrakty
Most of underground hydrocarbon storage are located in depleted natural gas reservoirs. Seismic survey is the most economical source of detailed subsurface information. The inversion of seismic section for obtaining pseudoacoustic impedance section gives the possibility to extract detailed subsurface information. The seismic wavelet parameters and noise briefly influence the resolution. Low signal parameters, especially long signal duration time and the presence of noise decrease pseudoimpedance resolution. Drawing out from measurement or modelled seismic data approximation of distribution of acoustic pseuoimpedance leads us to visualisation and images useful to stratum homogeneity identification goal. In this paper, the improvement of geologic section image resolution by use of minimum entropy deconvolution method before inversion is applied. The author proposes context and adaptive transformation of images and edge detection methods as a way to increase the effectiveness of correct interpretation of simulated images. In the paper, the edge detection algorithms using Sobel, Prewitt, Robert, Canny operators as well as Laplacian of Gaussian method are emphasised. Wiener filtering of image transformation improving rock section structure interpretation pseudoimpedance matrix on proper acoustic pseudoimpedance value, corresponding to selected geologic stratum. The goal of the study is to develop applications of image transformation tools to inhomogeneity detection in salt deposits.
Wydawca
Czasopismo
Rocznik
Tom
Strony
29--36
Opis fizyczny
Bibliogr. 14 poz., rys.
Twórcy
autor
- AGH University of Science and Technology, Faculty of Geology, Geophysics and Environment Protection, Kraków, Poland
Bibliografia
- [1] BOYLE R., SONKA M., HLAVAC V., Image Processing, Analysis, and Machine Vision, First Edition, University Press, Cambridge, 1993.
- [2] BROADHEAD M.K., PFLUG L.A., Deconvolution for transient classification using fourth order statistics, Naval Research Laboratory, Acoustics Division, Stennis Space Center, MS 39529-5009, USA.
- [3] CANNY J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 1986, 8, 679–714.
- [4] DERICHE R., Using Canny’s criteria to derive an optimal edge detector recursively implemented, Int. J. Computer Vision, April 1987, Vol. 1, 167–187.
- [5] FIGIEL W., KAWALEC-LATAŁA E., Context and adaptive transformation applied to interpretation of acoustic pseudoimpedance images of rocky surroundings, Gospodarka Surowcami Mineralnymi, 2009, t. 25, z. 3, 273–288.
- [6] FIGIEL W., KAWALEC-LATAŁA E., Zastosowanie analizy i przetwarzania obrazów do interpretacji syntetycznych sekcji pseudoimpedancji akustycznej, Gospodarka Surowcami Mineralnymi, 2008, t. 24, z. 2/3, 371–385.
- [7] GONZALES R.C., WINTZ P., Digital Image Processing, Second Edition, Addison-Wesley Publishing Co., Massachusets, 1987.
- [8] HUNT B.R., The Application of Constrained Least Squares Estimation to Image Restoration by Digital Computer, IEEE Transactions on Computers, September 1973, Vol, C-22, No. 9.
- [9] KAWALEC-LATAŁA E., The influence of seismic wavelet on the resolution of pseudo impedance section for construction of underground storage, Gospodarka Surowcami Mineralnymi, 2008, t. 24, z. 2/3, 387–397.
- [10] LINDEBERG T., Edge detection and ridge detection with automatic scale selection, International Journal of Computer Vision, 1998, 30, 2, 117–154.
- [11] PITAS J.I., Digital Image Processing Algorithms, Prentice Hall International (UK), Ltd., Cambridge, 1993.
- [12] VEEKEN P.C.H., DA SILVA M., Seismic inversion and some of their constrains, First Break, 22 (6), 47–70.
- [13] WIGGINS R.A., Minimum Entropy Deconvolution, Geoexploration, 1978, Vol. 16, 21–35.
- [14] ZIOU D., TABBONE S., Edge Detection Techniques An Overview, International Journal of Pattern Recognition and Image Analysis, 1998, 8(4), 537–559
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-26376647-4539-4521-906f-c3de6726282c