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Comparison of Factorial Kriging Analysis Method and Upward Continuation Filter to Recognize Subsurface Structures – A Case Study: Gravity Data from a Hydrocarbon Field in the Southeast Sedimentary Basins of the East Vietnam Sea

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
To interpret geophysical anomaly maps, it is necessary to filter out regional and sometimes noise components. Each measured value in a gravity survey consists of different components. Upward continuation (UC) is one of the most widely used filters. The shortcoming of this filter is not to consider the spatial structure of the data, and also the fact that the trial and error approach and expert’s judgment are needed to adjust it. This study aims to compare the factorial kriging analysis (FKA) and UC filters for separation of local and regional anomalies in the gravity data of a hydrocarbon field in the southeast sedimentary basins of the East Vietnam Sea. As shown in this paper, FKA method permits to filter out all of the identified structures, while the UC filter does not possess this capability. Therefore, beside general and classic filtering methods, the FKA method can be used as a strong method in filtering spatial structures and anomaly component.
Czasopismo
Rocznik
Strony
398--416
Opis fizyczny
Bibliogr. 32 poz.
Twórcy
autor
  • Shahrood University, Faculty of Mining and Petroleum Engineering and Geophysics, Shahrood, Iran
autor
  • Shahrood University, Faculty of Mining and Petroleum Engineering and Geophysics, Shahrood, Iran
  • Department of Chemical and Petroleum Engineering, University of Wyoming, Laramie, Wyoming, USA
  • Shahrood University, Faculty of Mining and Petroleum Engineering and Geophysics, Shahrood, Iran
autor
  • Shahrood University, Faculty of Mining and Petroleum Engineering and Geophysics, Shahrood, Iran
Bibliografia
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Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-05fb8ef3-906a-4aab-b433-c8d610693e20
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