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"Ground moving radartarget classification is one of the recent research issues that has arisen inan airborne ground moving target indicator (GMTI) scenario. This work presentsa novel technique for classifying individual targets depending on their radarcross section (RCS) values. The RCS feature is evaluated using the Chebyshevpolynomial. The radar captured target usually provides an imbalanced solutionfor classes that have lower numbers of pixels and that have similarcharacteristics. In this classification technique, the Chebyshev polynomial?sfeatures have overcome the problem of confusion between target classes withsimilar characteristics. The Chebyshev polynomial highlights the RCS featureand is able to suppress the jammer signal. Classification has been performed byusing the probability neural network (PNN) model. Finally, the classifier withthe Chebyshev polynomial feature has been tested with an unknown RCS value. Theproposed classification method can be used for classifying targets in a GMTIsystem under the warfield condition."

"Ground moving radar target classification is one of the recent research issues that has arisen in an airborne ground moving target indicator (GMTI) scenario. This work presents a novel technique for classifying individual targets depending on their radar cross section (RCS) values. The RCS feature is evaluated using the Chebyshev polynomial. The radar captured target usually provides an imbalanced solution for classes that have lower numbers of pixels and that have similar characteristics. In this classification technique, the Chebyshev polynomial’s features have overcome the problem of confusion between target classes with similar characteristics. The Chebyshev polynomial highlights the RCS feature and is able to suppress the jammer signal. Classification has been performed by using the probability neural network (PNN) model. Finally, the classifier with the Chebyshev polynomial feature has been tested with an unknown RCS value. The proposed classification method can be used for classifying targets in a GMTI system under the warfield condition."

"Lapangan “X” merupakan salah satu lapangan yang terletak di Cekungan Sunda. Lapangan ini merupakan salah satu zona potensi hidrokarbon di Indonesia, dengan salah satu zona potensi hidrokarbon terletak pada batuan reservoir karbonat build-up yang berada di Formasi Upper Baturaja. Studi ini akan menghasilkan volume petrofisika semu untuk properti petrofisika volume shale, porositas, dan saturasi air menggunakan lima kombinasi atribut seismik yang ditentukan melalui analisis multi-atribut, yang kemudian nilai korelasi dan errornya akan ditingkatkan probabilistic neural network (PNN). Integrasi dari ketiga metode ini bertujuan untuk memberikan gambaran dan pemahaman baru terhadap karakterisasi daerah yang berpotensi hidrokarbon di Lapangan ”X”.

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”X” Field is one of the fields located in Sunda Basin. “X” Field is one of the hydrocarbon potential zones in Indonesia, with one of its hydrocarbons potential zones located in the carbonate build up reservoir in the Upper Baturaja Formation. This study will produce pseudo petrophysical volumes for petrophysical properties such as shale volume, porosity, and water saturation using five seismic attributes combination from the seismic multi-attributes method. Probabilistic neural network (PNN) is used to improve the correlation and error value from the log. The integration of these three methods aims to provide new insights and understanding of the characterization of hydrocarbon potential areas in “X” Field."