Spectral Decomposition
Higher quality and better definition of geo-bodies, deposit geometries, deposit patterns and sedimentary models. Lateral continuity, thickness of strata, and structural elements. These are some of the advantages that the Spectral Decomposition analysis provides.
This decomposes the seismic signal into its frequency components, which can identify stratigraphic and subtle structural features that may be hidden in data displays with full bandwidth. The method is based on the fact that a thin-layer reflection has a unique spectral response in the frequency domain, and uses a mathematical algorithm that reveals distribution of frequencies.
Spectral Decomposition is a method to process seismic data in different frequency planes. When the Spectral Decomposition algorithm is applied to the seismic reflection data.
The use of different methods of spectral decomposition helps to visualize the morphology of a study area and depending on the choice of method can greatly improve the understanding of the potential of oil deposits associated with the presence of stratigraphic features. Comparing the gathers generated from each method, the differences in frequency resolution between the five techniques are shown
Figure 1. The following figure shows four gathers of frequencies generated from each mathematical algorithm, which are used to generate the results of the spectral decomposition.
The service includes six different types of analysis for spectral decomposition.
Spectral decomposition allows the detection of thin layers and geological features beyond the limits of traditional post-stack seismic analysis.
• Discrete Fourier Transform
• Continuous Wave Transform
• Continuous Waveform Transform Frequency-Time
• Transformed S
• Continuous Waveform Package Transform
• Realistic Seismic Wave Transform
The ColorCube tool allows the creation of high resolution maps based on frequencies. These visual displays make blending of low, medium and high frequencies.
Figure 2. Comparative analyzes of the maps generated with the attribute of Spectral Frequency Decomposition.
