Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Studia Geotechnica et Mechanica

The Journal of Wroclaw University of Technology

4 Issues per year

Open Access
Online
ISSN
2083-831X
See all formats and pricing
More options …

Edge Detection on Images of Pseudoimpedance Section Supported by Context and Adaptive Transformation Model Images

Ewa Kawalec-Latała
  • AGH University of Science and Technology, Faculty of Geology, Geophysics and Environment Protection, Kraków, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2014-06-13 | DOI: https://doi.org/10.2478/sgem-2014-0004

Abstract

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.

Keywords: underground storage; acoustic impedance; data analysis and visualisation; edge detection algorithms; rock salt; inhomogeneity detection

References

  • [1] BOYLE R., SONKA M., HLAVAC V., Image Processing, Analysis, and Machine Vision, First Edition, University Press, Cambridge, 1993.Google Scholar

  • [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.Google Scholar

  • [3] CANNY J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 1986, 8, 679-714.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [7] GONZALES R.C., WINTZ P., Digital Image Processing, Second Edition, Addison-Wesley Publishing Co., Massachusets, 1987.Google Scholar

  • [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.Google Scholar

  • [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.Google Scholar

  • [10] LINDEBERG T., Edge detection and ridge detection with automatic scale selection, International Journal of Computer Vision, 1998, 30, 2, 117-154.Google Scholar

  • [11] PITAS J.I., Digital Image Processing Algorithms, Prentice Hall International (UK), Ltd., Cambridge, 1993.Google Scholar

  • [12] VEEKEN P.C.H., DA SILVA M., Seismic inversion and some of their constrains, First Break, 22 (6), 47-70.Google Scholar

  • [13] WIGGINS R.A., Minimum Entropy Deconvolution, Geoexploration, 1978, Vol. 16, 21-35.CrossrefGoogle Scholar

  • [14] ZIOU D., TABBONE S., Edge Detection Techniques An Overview, International Journal of Pattern Recognition and Image Analysis, 1998, 8(4), 537-559.Google Scholar

About the article

Published Online: 2014-06-13

Published in Print: 2014-03-01


Citation Information: Studia Geotechnica et Mechanica, ISSN (Online) 2083-831X, ISSN (Print) 0137-6365, DOI: https://doi.org/10.2478/sgem-2014-0004.

Export Citation

© by Ewa Kawalec-Latała. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. BY-NC-ND 3.0

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Raymundo Domínguez-C, Manuel Romero-Salcedo, Luis G Velasquillo-Martínez, and Leonid Shemeretov
Journal of Geophysics and Engineering, 2017, Volume 14, Number 2, Page 417

Comments (0)

Please log in or register to comment.
Log in