E3S Web Conf.
Volume 66, 2018AG 2018 - 4th International Conference on Applied Geophysics
|Number of page(s)||10|
|Section||Geophysics in Geoengineering|
|Published online||26 November 2018|
Nonlinear anisotropic diffusion techniques for seismic signal enhancing - Carpathian Foredeep study
AGH University of Science and Technology, Department of Geoinformatics and Applied Computer Science, Cracow, Poland
* Corresponding author: firstname.lastname@example.org
The use of nonlinear anisotropic diffusion algorithm for advanced seismic signal processing in the complicated geological region of Carpathian Foredeep was examined. This technique allows for an improvement of seismic data quality and for more accurate interpretation by the recovery of a significant amount of structural information in the form of a correlating seismic reflections and by preserving true DHI indicators. It also allows searching for more subtle geological structures. Anisotropic diffusion is an iterative image processing algorithm that removes noise by modifying the data by solving partial differential equations. Moreover, it can reduce image noise without blurring the edges between regions of different chrominance or brightness. This filter preserves edges, lines, or other features relevant to the seismic structural and stratigraphic interpretation. The algorithm also enables noise reduction without removing significant information from a seismic section even for high dips values. For a better estimation of anisotropic diffusion structure tensor, the parameterization is done using the depth field and the calculations in the two-way travel time field. The presented research shows the results of using an anisotropic diffusion algorithm for post-stack and migration processing of seismic 3D data collected in Carpathian reservoir rocks of southern Poland.
Key words: Signal processing / Seismic / Anisotropic diffusion / Carpathian Foredeep / Nonlinear algorithm / Seismic interpretation
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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