| Issue |
E3S Web Conf.
Volume 133, 2019
AG 2019 – 5th International Conference on Applied Geophysics
|
|
|---|---|---|
| Article Number | 01006 | |
| Number of page(s) | 10 | |
| Section | Application of Geophysical Methods | |
| DOI | https://doi.org/10.1051/e3sconf/201913301006 | |
| Published online | 25 November 2019 | |
Some statistical consideration of azimuth and inclination angles determination based on walk - away VSP data in Python
1 AGH University of Science and Technology, Department of Geoinformatics and Applied Computer Science, Krakow, Poland
2 AGH University of Science and Technology, Department of Fossil Fuels, Krakow, Poland
3 GK Processing, R&D Department of Seismic Signal Processing Center, Balice, Poland
* Corresponding author: mattzareba@gmail.com
It is a common knowledge that proper inclinations and azimuth angle determination is a critical step in processing and interpretation of walk-away VSP data. Additionally, an in-depth analysis of the uncertainty of these interpreted values requires the introduction of measurement errors. In this contribution, we present a statistical analysis of obtained polarization angles from three-component, multi-depth level, walk-away VSP using Python 3 programming language. Our analysis is presented in the context of different processing sequences and correlation with local features of the geological medium. We show that the obtained values of polarization angles and their errors can be strongly affected by processing sequence and - when done correctly - can give addition inside into features of analysis medium. Moreover, in some cases, even a presence of saturation can be express by polarization angles variations. Additionally, we examined the impact of well-casing on interpretational values of polarization angles.
Key words: statistical analysis / seismic anisotropy / exploratory data analysis / VSP seismic method / data science / machine learning
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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